Some slides adapted from A. Silberschatz et al. Database System Concepts, 5th Ed. Midterm Review Database Management Systems I Alex Coman, Winter 2006

Some slides adapted from A. Silberschatz et al. Database System Concepts, 5th Ed.

Midterm ReviewMidterm Review

Database Management Systems I

Alex Coman, Winter 2006

Alex Coman Winter 2006

2

Midterm Review Database Management Systems I

SummarySummary

Introduction (Ch. 1)

Entity-Relationship Model (Sec. 6.1-6.9)

Relational Model and Algebra (Ch. 2)

Introduction to SQL (Ch. 3)

Advanced SQL (Sec. 4.1-4.5,4.8)


3


Purpose of Database SystemsPurpose of Database Systems

In the early days, database applications were built directly on top of file systems

Drawbacks of using file systems to store data:

Data redundancy and inconsistency

Difficulty in accessing data

Data isolation — multiple files and formats

Integrity problems

Atomicity of updates

Concurrent access by multiple users

Security problems

Database systems offer solutions to all the above problems


4


Overall Structure of a DBMSOverall Structure of a DBMS


5


Database AdministratorDatabase Administrator

Coordinates all the activities of the database system; the database administrator has a good understanding of the enterprise’s information resources and needs.

Database administrator's duties include:

Schema definition

Storage structure and access method definition

Schema and physical organization modification

Granting user authority to access the database

Maintenance

Specifying integrity constraints

Acting as liaison with users

Monitoring performance and responding to changes in requirements


6


Entity-Relatioship ModelEntity-Relatioship Model Design Process

characterize the data needs

conceptual design (E-R modeling)

specification of functional requirements

abstract model to implementation (reduction to relational schemas)

E-R Modeling entity and entity set

relationship and relationship set

attributes

constraints: keys, cardinality and participation

weak entity sets

specialization/generalization

aggregation

Reduction to Relation Schemas


7


ER Modeling (1)ER Modeling (1) Mapping cardinality constraints

Express the number of entities to which another entity can be associated via a relationship set.

For a binary relationship: one-to-one, one-to-many, many-to-one, many-to-many

Participation constraints

Total vs. partial participation

Keys for relationship sets

The combination of primary keys of the participating entity sets forms a super key of a relationship set

Must consider the mapping cardinality of the relationship set when deciding what are the candidate keys


8


ER Modeling (2)ER Modeling (2) Weak entity set

An entity set that does not have a primary key and whose existence depends on the existence of an identifying entity set

The primary key of a weak entity set is formed by the primary key of the identifying entity set plus the weak entity set’s discriminator (partial key)

“Each geographical location is described by city, province and country and uniquely identified by a location-id. In this context, each 330music store is identified by its address which is unique for a location.”

Mapping to relations:

• location (loc-id,…)

• stores (loc-id, address,…)

• has is redundant (why?)


9


ER Modeling (3)ER Modeling (3) Specialization/generalization

A lower-level entity set inherits all the attributes (including the primary key) of the higher-level entity set to which it is linked

Lower-level entity sets may have attributes or participate in relationships that do not apply to the higher-level entity set

“The preferred customers are uniquely identified by a customer-id, and their names and phone numbers are also available. The preferred customers may belong in one or both of two programs: Fastlane … and Newsletter …”


• customer (cust-id, name,phone)• fastlane (cust-id, cc)• newsletter (cust-id, email)

Alternative representation(is it?):

• fastlane (cust-id, name,phone, cc)

• newsletter (cust-id, name, phone, email)


10


ER Modeling (4)ER Modeling (4) Redundancy of schemas

Many-to-one and one-to-many relationship sets that are total on the many-side can be represented by adding an extra attribute to the “many” side, containing the primary key of the “one” side

“Each track belongs to a genre (and one genre only) …”


• tracks (track-id, artist, title, length, year, genre-id)

• genre (genre-id, main, sub, style)

• has is redundant (why?)


11


ER Design Issues and DecisionsER Design Issues and Decisions

Use of entity sets vs. attributes

Choice mainly depends on the structure of the enterprise being modeled, and on the associated semantics

Use of entity sets vs. relationship sets

Possible guideline is to designate a relationship set to describe an action that occurs between entities

Binary versus n-ary relationship sets

Use n-ary relationship set when it shows more clearly that several entities participate in a single relationship (eg: 330music history).

Mapping cardinalities affect ER design

Placement of relationship attributes

Use of a strong or weak entity set

Use of specialization/generalization


12


E-R Diagram for a Banking EnterpriseE-R Diagram for a Banking Enterprise

Mapping to relations (partial):

• payment (loan-number, payment-number, date, amount)

• Is loan_payment redundant?

• account (acc-number, balance)

• checking (acc-number, overdraft)

• savings (acc-number, interest)

• depositor (cust-id, acc-number , access-date)


13


Relational ModelRelational Model Structure of relational databases

a relation is a set of n-tuples (a1, a2, …, an) where each ai Di

R = (A1, …, An ) is a relation schema, where A1,…, An are attributes

Fundamental relational-algebra operations

select (), project (), union (), set difference (–), Cartesian product (x), rename ()

Additional relational-algebra operations

set intersection (), natural join ( ), division (), assignment ()

Extended relational-algebra operations

generalized projection, aggregate functions, outer join

Null values

a value does not exist or an unknown value

Modification of the database

deletion, insertion, update


14


Cartesian Product vs. Natural JoinCartesian Product vs. Natural Join Relations r, s:

r x s:

A B

1

2

A B

11112222

A C

1010201010102010

D

aabbaabb

A C

10102010

D

aabbr

s

r s:

A B

122

C

101020

D

aab


15


Rename OperationRename Operation

Renaming a relation (or expression)

x (E)returns the expression E under the name X

Renaming a relation (or expression) and its attributes

If a relational-algebra expression E has arity n, then

returns the result of expression E under the name X, and with the

attributes renamed to A1 , A2 , …., An .

)(),...,,( 21E

nAAAx


16


Aggregate Functions and OperationsAggregate Functions and Operations

Aggregation functions:

avg, min, max, sum, count

take a collection of values and returns a single value as a result

Aggregate operation in relational algebra (calligraphic G)

G1, G2 …, Gn is a list of attributes on which to group (can be empty)

Each Fi is an aggregate function

Each Ai is an attribute name

use renaming operator or as part of aggregate operation (with as)

branch_name g sum(balance) as sum_balance (account)

sum_account(branch_name, sum_balance) (branch_name g sum(balance) (account))

)()(,),(),(,,, 221121E

nnn AFAFAFGGG g


17


Division OperationDivision Operation

Suited to queries that include the phrase “for all” (or “every”).

A B

aaaaaaaa

C D

aabababb

E

11113111

Relations r, s:

r s:

D

ab

E

11

A B

aa

C

r

s


18


Example (1)Example (1)

Find the accounts held by more than two customers

a. using an aggregate function

b. without using any aggregate functions


19



Find the names of employees who have borrowed all books published by McGraw-Hill.

For each publisher, find the names of employees who have borrowed more than five books of that publisher


20


SQL (1)SQL (1) Data Definition Language (DDL)

constructs: create table, drop table, alter table

constraints: primary key, unique, not null

Basic Query Structure: select … from … where …

distinct vs. all

rename operation: as

tuple variables

Set Operations

union, intersect, except

Aggregate Functions

avg, min, max, sum, count

group by and having clauses


21


SQL (2)SQL (2)

Null Values

is null and is not null predicates

Nested Subqueries

set membership: in, not in

set comparison: some, any

empty relations: exist, not exist

set containment and division:

not exist (B except A) to test if B A

absence of duplicates: unique, not unique

Complex Queries

derived relations

with clause


22


SQL (3)SQL (3) Views

virtual relations

create view, drop view constructs

Joined Relations

join types: inner join, left/right/full outer join

join conditions: natural, on <predicate>, using (A1,…,Ak)

Modification of the Database

deletion

insertion

updates

case construct


23


Cartesian Product vs. Natural JoinCartesian Product vs. Natural Join Relations r, s:

A B

1

2

A B

11112222

A C

1010201010102010

D

aabbaabb

A C

10102010

D

aabb

r s

A B

122

C

101020

D

aab

select * from r, s

select A,B,C,D from r, s where r.a=s.a

select * from r natural inner join s


24



Find the names of employees who have borrowed all books published by McGraw-Hill.


25



For each publisher, find the names of employees who have borrowed more than five books of that publisher


26


Advanced SQL (1)Advanced SQL (1) SQL Data Types and Schemas

Basic: int, char (n), numeric (p,d), float (p), real

Advanced: date, time, timestamp, interval, blob, clob

User-defined: create type, create domain

Integrity Constraints

check (P)

referential integrity: primary/foreign/unique key

assertions

Authorization

access: read, insert, update, delete

modification: resources, drop, alteration, index

grant and revoke statements


27


Advanced SQL (2)Advanced SQL (2) Embedded SQL

pre-defined statements

EXEC SQL statement

open, fetch, close operations on cursors

Dynamic SQL

run-time statements

ODBC vs. JDBC APIs

connect to database

execute SQL statement

fetch results

prepared statements, metadata features, transactions

Advanced SQL Features*


28


More Examples (1)More Examples (1)

Find all branches where the total account deposit is less than the average total account deposit at all branches using a nested query in the from clause


29


More Examples (2)More Examples (2)

Find all branches where the total account deposit is less than the average total account deposit at all branches using a nested query in the having clause

Some slides adapted from A. Silberschatz et al. Database System Concepts, 5th Ed.

EndEnd

Documents

Some slides adapted from A. Silberschatz et al. Database System Concepts, 5th Ed. Midterm Review Database Management Systems I Alex Coman, Winter 2006