1. CS405G: Introduction to Database Systems

2. Announcement
Today
Review
Friday
Go over homework
Course evaluation

3. Materials Review for final Book
Slides ( should be all on the course website)
Homework
Quizzes
Mid-Term

4. Jinze Liu @ University of Kentucky
Database Design
11/16/2018
Jinze University of Kentucky

5. E-R model
E-R model
Entities
Attributes
Relationships

6. Jinze Liu @ University of Kentucky
11/16/2018
Jinze University of Kentucky

7. Database Design
11/16/2018
11/16/2018 7 7 7

8. From E-R Diagram to Relations
Schemas
Converting E-R diagram to relations
Keys
Super keys
Candidate keys
Primary keys
Relational integrity constraints

9. Jinze Liu @ University of Kentucky
Key Constraints
Superkey:
(Uniqueness constraints) A set of attributes where no two distinct tuples can have the same values
Every relation has at least one superkey:
The set of all attributes.
Key: A minimal superkey
Uniqueness constraint (superkey)
Minimum Constraint
No attribute can be removed and still satisfy the uniqueness constraints.
11/16/2018
Jinze University of Kentucky 6

10. Relational Integrity Constraints
Constraints are conditions that must hold on all valid relation instances. There are four main types of constraints:
Domain constraints
The value of a attribute must come from its domain
Key constraints
Entity integrity constraints
Referential integrity constraints
11/16/2018
11/16/2018 10 10 10

11. Database Normalization
Functional Dependency
Functional Closure
Keys
Redefined
Based on functional dependency
DB Norm Form
1st, 2nd, 3rd, BCNF

12. Three Types of non-key DFX A
X  A
Partial dependency
key X A
X  A
Transitive dependency I
key X A
X  A
Transitive dependency II
11/16/2018
Luke Huan Univ. of Kansas

13. Luke Huan Univ. of Kansas
3NF
R is in Third Normal Form (3NF) if for every non-trivial FD X -> A (where A is single attribute), either
X is a superkey of R, or
A is a member of at least one key of R
Intuitively, BCNF decomposition on X -> A would “break” the key containing A X A X A X A
Partial dependency
Transitive dependency I
Transitive dependency II
2NF
3NF
BCNF
2NF  3NF  BCNF
11/16/2018
Luke Huan Univ. of Kansas

14. Luke Huan Univ. of Kansas
Database Query
11/16/2018
Luke Huan Univ. of Kansas

15. Relational Algebra and SQL
SQL query
SFW
Group by …, Having
Subqueries
Relationship between R.A. and SQL

16. Jinze Liu @ University of Kentucky
Relational algebra
A language for querying relational databases based on operators:
RelOp
RelOp
Core set of operators:
Selection, projection, cross product, union, difference, and renaming
Additional, derived operators:
Join, natural join, intersection, etc.
Compose operators to make complex queries
We are gonna cover this in one day!
Possible because of the minimalist approach.
11/16/2018
Jinze University of Kentucky

17. Summary of core operators
Selection:
Projection:
Cross product:
Union:
Difference:
Renaming:
Does not really add “processing” power
σp R
πL R
R X S
R S
R - S
ρ S(A1, A2, …) R
11/16/2018
Jinze University of Kentucky

18. Summary of derived operators
R p S
R  S
R S
Join:
Natural join:
Intersection:
11/16/2018
Jinze University of Kentucky

19. Classification of relational operators
Selection: σp R
Projection: πL R
Cross product: R X S
Join: R p S
Natural join: R  S
Union: R U S
Difference: R - S
Intersection: R ∩ S
Monotone
Monotone w.r.t. R; non-monotone w.r.t S
11/16/2018
Jinze University of Kentucky

20. Update Operations on Relations
INSERT a tuple.
DELETE a tuple.
MODIFY a tuple.
Constraints should not be violated in updates
11/16/2018
Jinze University of Kentucky

21. Basic queries: SFW statement
SELECT A1, A2, …, An FROM R1, R2, …, Rm WHERE condition;
Also called an SPJ (select-project-join) query
(almost) Equivalent to relational algebra query π A1, A2, …, An (σ condition (R1 X R2 X … X Rm))
11/16/2018
Luke Huan Univ. of Kansas

22. Luke Huan Univ. of Kansas
Semantics of SFW
SELECT E1, E2, …, En FROM R1, R2, …, Rm WHERE condition;
For each t1 in R1: For each t2 in R2: … … For each tm in Rm: If condition is true over t1, t2, …, tm: Compute and output E1, E2, …, En as a row
t1, t2, …, tm are often called tuple variables
Not 100% correct, we will see
11/16/2018
Luke Huan Univ. of Kansas

23. Operational semantics of GROUP BY
SELECT … FROM … WHERE … GROUP BY …;
Compute FROM
Compute WHERE
Compute GROUP BY: group rows according to the values of GROUP BY columns
Compute SELECT for each group
For aggregation functions with DISTINCT inputs, first eliminate duplicates within the group
Number of groups = number of rows in the final output
11/16/2018
Jinze University of Kentucky

24. Jinze Liu @ University of Kentucky
Database Design
11/16/2018
Jinze University of Kentucky

25. Jinze Liu @ University of Kentucky
A DBMS Overview
11/16/2018
Jinze University of Kentucky

26. physical data organization
Storage hierarchy (Lexington vs. Pluto) ! count I/O’s
Disk geometry: three components of access cost; random vs. sequential I/O
Data layout
Record layout (handling variable-length fields, NULL’s)
Block layout (NSM, PAX) ! inter-/intra-record locality
Access paths
Primary versus secondary indexes
Tree-based indexes: ISAM, B+-tree
! Again, reintroduce redundancy to improve performance
! Fundamental trade-off: query versus update cost