The Relational Model Suan Lee
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The Relational Model
Suan Lee
Database Management System (DBMS)
• Used by all major commercial database systems
• Very simple model
• Query with high-level languages: simple yet expressive
• Efficient implementations
The Relational Model - Suan Lee 2
• Database = set of named relations (or tables)
The Relational Model - Suan Lee 3
• Database = set of named relations (or tables)
The Relational Model - Suan Lee 4
Student College
• Database = set of named relations (or tables)
• Each relation has a set of named attributes (or columns)
The Relational Model - Suan Lee 5
ID Name GPA Photo Name State ENR
Student College
• Database = set of named relations (or tables)
• Each relation has a set of named attributes (or columns)
• Each tuple (or row) has a value for each attribute
The Relational Model - Suan Lee 6
ID Name GPA Photo
1 Adam 3.9
2 Bill 3.4 NULL
3 Cathy NULL
Name State ENR
Stanford CA 15,000
Berkeley CA 36,000
MIT MA 10,000
Student College
… … … … … … …
• Database = set of named relations (or tables)
• Each relation has a set of named attributes (or columns)
• Each tuple (or row) has a value for each attribute
• Each attribute has a type (or domain)
The Relational Model - Suan Lee 7
ID Name GPA Photo
1 Adam 3.9
2 Bill 3.4 NULL
3 Cathy NULL
Name State ENR
Stanford CA 15,000
Berkeley CA 36,000
MIT MA 10,000
Student College
… … … … … … …
• Schema = structural description of relations in database
• Instance = actual contents at given point in time
The Relational Model - Suan Lee 8
ID Name GPA Photo
1 Adam 3.9
2 Bill 3.4 NULL
3 Cathy NULL
Name State ENR
Stanford CA 15,000
Berkeley CA 36,000
MIT MA 10,000
Student College
… … … … … … …
• NULL – special value for “unknown” or “undefined”
The Relational Model - Suan Lee 9
ID Name GPA Photo
1 Adam 3.9
2 Bill 3.4 NULL
3 Cathy NULL
Name State ENR
Stanford CA 15,000
Berkeley CA 36,000
MIT MA 10,000
Student College
… … … … … … …
• Key – attribute whose value is unique in each tuple Or set of attributes whose combined values are unique
The Relational Model - Suan Lee 10
ID Name GPA Photo
1 Adam 3.9
2 Bill 3.4 NULL
3 Cathy NULL
Name State ENR
Stanford CA 15,000
Berkeley CA 36,000
MIT MA 10,000
Student College
… … … … … … …
• Creating relations (tables) in SQL
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CREATE TABLE Student (ID INTEGER, name STRING, GPA FLOAT, photo STRING)
CREATE TABLE College (name STRING, state CHAR(2), enrollment INTEGER)
• Used by all major commercial database systems
• Very simple model
• Query with high-level languages: simple yet expressive
• Efficient implementations
The Relational Model - Suan Lee 12
Steps in creating and using a (relational) database
Querying Relational Databases - Suan Lee 13
Steps in creating and using a (relational) database
1. Design schema; create using DDL
Querying Relational Databases - Suan Lee 14
Steps in creating and using a (relational) database
1. Design schema; create using DDL
2. “Bulk load” initial data
Querying Relational Databases - Suan Lee 15
Steps in creating and using a (relational) database
1. Design schema; create using DDL
2. “Bulk load” initial data
3. Repeat: execute queries and modifications
Querying Relational Databases - Suan Lee 16
Steps in creating and using a (relational) database
1. Design schema; create using DDL
2. “Bulk load” initial data
3. Repeat: execute queries and modifications
Querying Relational Databases - Suan Lee 17
M
“ok”
Ad-hoc queries in high-level language
Querying Relational Databases - Suan Lee 18
Ad-hoc queries in high-level language
Querying Relational Databases - Suan Lee 19
All students with GPA > 3.7 applying to Stanford and MIT only
All engineering departments in CA with < 500 applicants
College with highest average accept rate over last 5 years
Ad-hoc queries in high-level language
• Some easy to pose; some a bit harder
• Some easy for DBMS to execute efficiently; some harder
• “Query language” also used to modify data
Querying Relational Databases - Suan Lee 20
All students with GPA > 3.7 applying to Stanford and MIT only
All engineering departments in CA with < 500 applicants
College with highest average accept rate over last 5 years
Queries return relations (“compositional”, “closed”)
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Q1
Queries return relations (“compositional”, “closed”)
Querying Relational Databases - Suan Lee 22
Q1
Q2
Query Languages
• Relational Algebra (formal)
• SQL (actual/implemented)
Querying Relational Databases - Suan Lee 23
Query Languages
• Relational Algebra (formal)
• SQL (actual/implemented)
IDs of students with GPA > 3.7 applying to Stanford
Querying Relational Databases - Suan Lee 24
Query Languages
• Relational Algebra (formal)
• SQL (actual/implemented)
IDs of students with GPA > 3.7 applying to Stanford
Querying Relational Databases - Suan Lee 25
ΠID(σGPA=3.7 ∧ College.Name=‘Stanford’(Student⋈ Apply))
Query Languages
• Relational Algebra (formal)
• SQL (actual/implemented)
IDs of students with GPA > 3.7 applying to Stanford
Querying Relational Databases - Suan Lee 26
SELECT Student.IDFROM Student, ApplyWHERE Student.ID=Apply.IDAND GPA>3.7 AND College.name=‘Stanford’
ΠID(σGPA=3.7 ∧ College.Name=‘Stanford’(Student⋈ Apply))
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