1. Principles of Database Management Systems CSE 544
Introduction
March 31st, 1999

2. Staff Instructor: Alon Levy TAs: Zack Ives and Rachel Pottinger
Sieg, Room 310,
Office hours: wed, 2:30-3:30. Or by .
TAs: Zack Ives and Rachel Pottinger
Office hours:
Zack: Mondays at noon (224)
Rachel: Thursdays at 2:30pm (224)
Mailing list:
Web page: (a lot of stuff already there)

3. Course Times
In general, WF, 12-1:20pm (with a 5 minute breather in the middle).
Two special dates:
Monday, April 5th
Monday, April 19th
No classes on last week.

4. Goals of the Course Purpose:
Foundations of database management systems.
Issues in building database systems.
Introduction to current research issues in databases.
Have fun: databases are not just bunches of tuples.

5. Grading Homeworks: 15% Project: 25% Midterm: 15% Final: 35%
SQL querying fun
Join implementations
Project: 25%
A query optimization engine for data integration.
Midterm: 15%
Final: 35%
Participation and intangibles: 10%

6. Textbook
Database System Implementation, Ullman, Widom, and Garcia-Molina, to be published by Prentice-Hall in June; available from the copy center.

7. Other Useful Texts Database Management Systems (Ramakrishnan)
Foundations of Databases (Abiteboul, Hull & Vianu)
Parallel and Distributed DBMS (Ozsu and Valduriez)
Transaction Processing (Gray and Reuter)
Database Systems (Silberschatz, Korth and Sudarshan)
Data and Knowledge based Systems (volumes I, II) (Ullman)
Readings in Database Systems (Stonebraker and Hellerstein)
Proceedings of SIGMOD, VLDB, PODS conferences.

8. Prerequisites

9. Real Prerequisites Operating systems Data structures and algorithms
Distributed systems
Complexity theory
Mathematical Logic
Knowledge Representation
User interface design
Programming languages
Artificial Intelligence (Search)
Greek, Hebrew, French

10. Why Use a DBMS? All programs manipulate data, so why use a database?
Large amounts of data (Giga’s, Tera’s)
Data is very structured
Persistent data
Valuable data
Performance requirements
Concurrent access to the data
Restricted access to data

11. Functionality of a DBMS
Persistent storage management
Transaction management
Resiliency: recovery from crashes.
Separation between logical and physical views of the data.
High level query and data manipulation language.
Efficient query processing
Interface with programming languages

12. Persistent Storage
Becomes a hard problem because of the interaction with the other levels of the DBMS:
What are we storing?
Efficient indexing
Special issues due to resiliency requirements
Exploit “semantic” knowledge
Issue: interaction with the operating system. Should we rely on the OS?

13. Transaction Processing and Recovery
For efficient use of resources, we want concurrent access to data.
Systems sometimes crash.
A “real” database guarantees ACID:
Atomicity: all or nothing of a transaction.
Consistency: always leave the DB consistent.
Isolation: every transaction runs as if it’s the only one in the system.
Durability: if committed, we really mean it.
Do we really want ACID?

14. Physical vs. Logical Levels
External Schema 1
External Schema 2
Conceptual schema: tables and their
attributes
Physical schema: files, indexes
hash tables.
External schema: views of the different
applications, classes of users.
Relational Schema
System catalog: The component of the database that stores meta data.
Conceptual design: a precursor to the relational schema.
Physical Schema
Disk

15. The Relational Model Data is organized into tables with attributes.
Rows in the tables are tuples.
The power of simplicity!

16. Logical Model Issues What data model should we use?
Relational, object-oriented, object-relational, deductive database model, semi-structured
How do we design a good schema? (normal forms, index selection)
Are we really providing an abstraction?
How does this abstraction interact with the programming language? (the impedance mismatch).

17. Querying a Database
Find all the students who have taken CSE444 in Winter, 1998.
S(tructured) Q(uery) L(anguage)
select E.name
from Enroll E
where E.course=CSE444 and
E.quarter=“Winter, 1998”
SQL also provides update facilities.
SQL: an acquired taste (try datalog first)

18. Issues in Query Languages
Does it provide the appropriate functionality?
SQL books get thicker and thicker.
Expressive power of a query language.
Ease of use (query by example)
Declarativity
Provide guidance in writing “good” queries?

19. Query Optimization
A query is a declarative specification of “what” you want.
A query execution plan is an imperative program to produce the answer.
Query optimization: produce an efficient query execution plan.
Issues: large search space of plans, cost estimation, semantic transformations
Real goal: avoid the bad plans.

20. Database Industry
Relational databases are a great success of theoretical ideas.
“Big 3” DBMS companies are among the largest software companies in the world.
IBM (with DB2) and Microsoft (SQL Server, Microsoft Access) are also important players.
$20B industry
Moving to warehousing, decision support.

21. Course (Rough) Outline
The basics:
The relational model
SQL
Views, integrity constraints
Conceptual modeling
datalog (recursive queries)
Physical representation:
Index structures.

22. Course Outline (cont) Query execution: Query Optimization
Algorithms for joins, selections, projections.
Query Optimization
Advanced topics:
data integration
data mining
semi-structured data
Transaction processing

23. The relational data model

24. Terminology Product (relation name) Attribute names
Name Price Category Manufacturer
gizmo $ gadgets GizmoWorks
Power gizmo $ gadgets GizmoWorks
SingleTouch $ photography Canon
MultiTouch $ household Hitachi
tuples
(Arity=4)
Product(name: string, Price: real, category: enum, Manufacturer: string)

25. More Terminology Every attribute has an atomic type.
Relation Schema: relation name + attribute names + attribute types
Relation instance: a set of tuples. Only one copy of any tuple! (not)
Database Schema: a set of relation schemas.
Database instance: a relation instance for every relation in the schema.

26. More on Tuples
Formally, a mapping from attribute names to (correctly typed) values:
name gizmo
price $19.99
category gadgets
manufacturer GizmoWorks
Sometimes we refer to a tuple by itself: (note order of attributes)
(gizmo, $19.99, gadgets, GizmoWorks) or
Product (gizmo, $19.99, gadgets, GizmoWorks).

27. Integrity Constraints
An important functionality of a DBMS is to enable the specification
of integrity constraints and to enforce them.
Knowledge of integrity constraints is also useful for query
optimization.
Examples of constraints:
keys, superkeys
foreign keys
domain constraints, tuple constraints.
Functional dependencies, multivalued dependencies.

28. Keys
A minimal set of attributes that uniquely the tuple (I.e., there is no
pair of tuples with the same values for the key attributes):
Person: social security number
name
name + address
name + address + age
Perfect keys are often hard to find, but organizations usually
invent something anyway.
Superkey: a set of attributes that contains a key.
A relation may have multiple keys: (but only one primary key)
employee number, social-security number

29. Foreign Key Constraints
Purchase:
buyer price product
Joe $ gizmo
Jack $ E-gizmo
Product:
name manufacturer description
gizmo G-sym great stuff
E-gizmo G-sym even better
An attribute of a relation R is must refer to a key of a relation S.

30. Functional Dependencies
Definition:
If two tuples agree on the attributes
A , A , … A 1 2 n
then they must also agree on the attributes
B , B , … B 1 2 m
Formally:
A , A , … A
B , B , … B 1 2 n 1 2 m
Key of a relation: all the attributes are either on the left or right.

31. Some Obvious Properties of FD’s
A , A , … A
B , B , … B
Is equivalent to 1 2 n 1 2 m
A , A , … A B 1 2 n 1
Splitting rule
and
Combing rule
A , A , … A B 1 2 n 2 …
A , A , … A B 1 2 n m
A , A , … A A
Always holds. 1 2 n i

32. Comparing Functional Dependencies
Entailment: a set of functional dependencies S1 entails a set S2 if:
any database that satisfies S1 much also satisfy S2.
Example: A B, B C entails A C
Equivalence: two sets of FD’s are equivalent if each entails the other.
{A B, B C } is equivalent to {A B, A C, B C}
Closure: Given a set of attributes A and a set of dependencies C,
we want to find all the other attributes that are functionally
determined by A.

33. Closure Algorithm Start with Closure=A.
Until closure doesn’t change do:
if is in C, and
B is not in Closure
then
add B to closure.
A , A , … A B 1 2 n
A , A , … A
Are all in the closure, and 1 2 n

34. Problems in Designing Schema
Name SSN Phone Number
Fred (201)
Fred (206)
Joe (908)
Joe (212)
Problems:
- redundancy
- update anomalies
- deletion anomalies

35. Relation Decomposition
Break the relation into two relations:
Name SSN
Fred
Joe
Name Phone Number
Fred (201)
Fred (206)
Joe (908)
Joe (212)

36. Boyce-Codd Normal Form
A simple condition for removing anomalies from relations:
A relation R is in BCNF if and only if:
Whenever there is a nontrivial dependency
for R , it is the case that { }
is a super-key for R.
A , A , … A B 1 2 n
A , A , … A 1 2 n
In English (though a bit vague):
Whenever a set of attributes of R is determining another attribute,
should determine all the attributes of R.