Database Design and MySQL by coronanlime

VIEWS: 347 PAGES: 55

									Database Design and MySQL

          Session 4
         INFM 718N
     Web-Enabled Databases

• Database design


• Project teams: next steps

• (if we have time) Programming
• Relational normalization
• Structured programming
• Software patterns
• Object-oriented design
• Functional decomposition

                                           Client Hardware            (PC)

                                            Web Browser               (IE, Firefox)

                                Client-side Programming               (JavaScript)
         Business Interaction

                                               Interchange Language   (HTML, XML)

                                     Server-side Programming          (PHP)

                                              Database                (MySQL)

                                          Server Hardware             (PC, Unix)
• 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

                  M                    1
        student          member-of           team

human                 implement-role         creates

                  1                    M
         client            needs           project


                               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
• 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
         Getting started with MySQL
• “root” creates database, grants permissions
  – By you on WAMP (mysql –u root –p)
  – By Charles Goldman on OTAL
    team1.* TO „foo‟@‟localhost‟ IDENTIFIED BY „bar‟;

• 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
  –   DESCRIBE tablename;
  –   SELECT * FROM tablename;

• Optimization
      • OPTIMIZE TABLE tablename;
  – EXPLAIN <SQLquery>;
      • ALTER TABLE tablename ADD INDEX fieldname;
                  Creating Tables
CREATE TABLE contacts (
  cstring VARCHAR(40) NOT NULL,
  PRIMARY KEY (ckey)

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'),

 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   First Name   Department IDDepartmentCourse ID Course description Grades email
    1      Arrows      John         EE           EE         lbsc690 Information Technology 90 jarrows@wam
    1      Arrows      John         EE           Elec Engin ee750 Communication            95 ja_2002@yahoo
    2      Peters      Kathy        HIST         HIST       lbsc690 Informatino Technology 95 kpeters2@wam
    2      Peters      Kathy        HIST         history    hist405 American History       80 kpeters2@wma
    3      Smith       Chris        HIST         history    hist405 American History       90 smith2002@glue
    4      Smith       John         CLIS         Info Sci   lbsc690 Information Technology 98 js03@wam
A Normalized Relational Database
 Student Table
Student ID       Last Name            First Name   Department ID     email
             1   Arrows               John         EE                jarrows@wam
             2   Peters               Kathy        HIST              kpeters2@wam
             3   Smith                Chris        HIST              smith2002@glue
             4   Smith                John         CLIS              js03@wam

Department Table                              Course Table
Department ID    Department                   Course ID            Course Description
EE               Electronic Engineering       lbsc690              Information Technology
HIST             History                      ee750                Communication
CLIS             Information Stuides          hist405              American History

                 Enrollment Table
                 Student ID          Course ID                Grades
                                 1   lbsc690                                90
                                 1   ee750                                  95
                                 2   lbsc690                                95
                                 2   hist405                                80
                                 3   hist405                                90
                                 4   lbsc690                                98
                           Example of Join
Student Table                                                            Department Table
Student ID     Last Name   First Name   Department ID   email            Department ID   Department
             1 Arrows      John         EE              jarrows@wam
                                                                         EE              Electronic Engineering
             2 Peters      Kathy        HIST            kpeters2@wam
             3 Smith       Chris        HIST            smith2002@glue   HIST            History
             4 Smith       John         CLIS            js03@wam         CLIS            Information Stuides

    “Joined” Table
   Student ID Last Name    First Name       Department IDDepartment                      email
       1      Arrows       John             EE           Electronic Engineering          jarrows@wam
       2      Peters       Kathy            HIST         History                         kpeters2@wam
       3      Smith        Chris            HIST         History                         smith2002@glue
       4      Smith        John             CLIS         Information Stuides             js03@wam
 New Table
Student ID Last Name    First Name   Department IDDepartment               email
    1      Arrows       John         EE           Electronic Engineering   jarrows@wam
    2      Peters       Kathy        HIST         History                  kpeters2@wam
    3      Smith        Chris        HIST         History                  smith2002@glue
    4      Smith        John         CLIS         Information Stuides      js03@wam
                                SELECT Student ID, Department
                       Student ID     Department
                           1          Electronic Engineering
                           2          History
                           3          History
                           4          Information Stuides
 New Table
Student ID Last Name   First Name   Department IDDepartment               email
    1      Arrows      John         EE           Electronic Engineering   jarrows@wam
    2      Peters      Kathy        HIST         History                  kpeters2@wam
    3      Smith       Chris        HIST         History                  smith2002@glue
    4      Smith       John         CLIS         Information Stuides      js03@wam

                              WHERE Department ID = “HIST”

Student ID Last Name       First Name Department IDDepartment               email
    2 Peters               Kathy      HIST         History                  kpeters2@wam
    3 Smith                Chris      HIST         History                  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”);

• 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” />

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
• How layout helps reading   Modular Programming
• How variables are named    • Functional decomposition
• How strings are used       • How functions are invoked
• How input is obtained      • How arguments work
• How output is created      • How scope is managed
                             • How errors are handled
Structured Programming       • How results are passed
• How things are nested
• How arrays are used
  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
   Initial       Choose
Requirements   Functionality              Create

                Prototype                        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!

To top