Download Overview of Databases I

Databases II
(Fall 2009)
Professor: Iluju Kiringa
[email protected]
http://www.site.uottawa.ca/~kiringa
SITE 5072
1
Review of Databases I
Chapters 2-5
2
What is a DBMS?
A centralized database is a large collection of
integrated data.
 Organizations face large quantities of data
that must be efficiently managed.

 Many store GBs, even TBs of data
 Some scientific applications store PBs of data !

A Database Management System (DBMS) is a
software package designed to store and
manage databases.
3
Content of « Databases I »

Foundations of DBMSs (Chap. 2-5, 19): :
 Conceptual design - Chap. 2:
• Input: requirements of an application
• Output: ER diagrams (i.e., ER model)
 Logical design – Chap. 3:
• Input: ER diagrammes
• Output: Relational model
 Normalization – Chap. 19:
• Input: Relational model
• Output: Relational model in normal forms
 Relational algebra and calculus – Chap. 4
 SQL – Chap. 5

Application development (Chap. 6-7)
 Classic applications: Embeded SQL, JDBC, SQLJ, stored procedures
 Internet applications : HTTP, HTML, XML, 3-tier architecture
4
Content of « Databases I » (Cont’d)

Storage and Indexing (Chap. 8-11):
 Disks and files – Chap. 9:
• Memory hierarchy
• Disk and file management
 Tree index – Chap. 10:
• ISAM tree
• B+ tree
 Hasch index – Chap. 11:
• static
• extendible
• linear
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Content of « Databases II »

Query evaluation (Chap. 12-15)
 External sort - Chap. 13
 Evaluation of relational operators -- Chap. 14
 System R: a sample evaluation algorithm – Chap. 15

Transactions (Chap. 16-18)
 Concurrency control – Chap. 17
 Recovery – Chap. 18

Advanced topics (Chap. 22,25, 26, 23 ou 27)
 Distributed databases – Chap. 22
 Data Warehousing – Chap. 25
 Data Mining Chap. 26
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Overview of Database Design





Requirements analysis: Find out what users want to do
with the database
Conceptual design: Use the output of RA to develop a
high-level description of the data to be stored, along with
their constraints. Output of CD usually is an ER-diagram.
Logical design: Choose a DBMS and map the conceptual
schema (ER-diagram) into the data model of the DBMS.
Output of this step is the logical schema of the data.
Schema refinement: Analyze the logical schema, identify
potential problems in it, and fix these by refining the
logical schema using known normal forms.
Physical design: Consider expected workloads that the
database will support to further refine the design in
order to meet desired performance criteria. Output here
is the physical schema.
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ER Model

The model

Basic elements:
•
•
•

Advanced elements:
•
•
•
•

Entity: real-world object
Relationship: association between entities
Attributes: description of an entity or a relationship
Constraints: qualification of a relationship
Weak entity : Identifiable only via another entity
Hierarchy : similar to OO-languages
Agregation: sort of macros
Conceptual design using the ER model




Should a given concept be modelled as an entity or an attribute?
Should a given concept be modelled as an entity or a relationship?
Should a given concept be modelled as a binary or as a ternary
relationship?
etc
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Relational Database Concepts

Relation: made up of 2 parts:






Instance : a table, with rows and columns.
#Rows = cardinality, #fields = degree / arity.
Schema : specifies name of relation, plus name and domain (type) of
each column (attribute).
Can think of a relation as a set of rows or tuples (i.e., all
rows are distinct), where each tuple has the same arity as
the relation schema.
Relational database: a set of relations, each with distinct
name.
Relational DB schema: set of schemas of relations in the DB.
Relational DB instance: set of relation instances in the DB.
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Sample Relation
 Schema :
Instance :
Students(sid: string, name: string, login: string,
age: integer, gpa: real).
sid
53666
53688
53650
name
login
Jones jones@cs
Smith smith@eecs
Smith smith@math
age
18
18
19
gpa
3.4
3.2
3.8
Cardinality = 3, arity = 5, all rows distinct.
 Commercial systems allow duplicates.
 Order of attributes may or may not matter!
 Do all columns in a relation instance have to
be distinct? Depends on whether they are ordered or not.

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Creation and Alteration of Relations
CREATE TABLE Students
(sid: CHAR(20),
name: CHAR(20),
login: CHAR(10),
age: INTEGER,
gpa: REAL)
DROP TABLE Students
CREATE TABLE Enrolled
(sid: CHAR(20),
cid: CHAR(20),
grade: CHAR(2))
ALTER TABLE Students
ADD COLUMN firstYear: integer
INSERT INTO Students (sid, name, login, age, gpa)
VALUES (53688, ‘Smith’, ‘smith@ee’, 18, 3.2)
DELETE FROM Students S
WHERE S.name = ‘Smith’
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Primary Key and Foreign Key in SQL
CREATE TABLE Enrolled
(sid CHAR(20)
cid CHAR(20),
grade CHAR(2),
PRIMARY KEY (sid),
UNIQUE (cid, grade) )
CREATE TABLE Enrolled
(sid CHAR(20)
cid CHAR(20),
grade CHAR(2),
PRIMARY KEY (sid,cid) )
CREATE TABLE Enrolled
(sid CHAR(20), cid CHAR(20), grade CHAR(2),
PRIMARY KEY (sid,cid),
FOREIGN KEY (sid) REFERENCES Students )
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Logical DB Design: ER to Relational
The ER model represent the initial, high-level
database design.
 The task is to generate a relational database
schema that is as close as possible to the ER
model.
 The mapping is approximate since it is hard to
translate all the constraints of the ER model into
an efficient logical (relational) model.

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Formal Relational Query Languages

Two mathematical Query Languages form
the basis for “real” languages (e.g. SQL), and
for implementation:
 Relational Algebra: More operational, very useful
for representing execution plans.
 Relational Calculus: Lets users describe what they
want, rather than how to compute it (Nonoperational, declarative).
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Relational Algebra

Basic operations:








Additional operations:


Selection ( ) Selects a subset of rows from relation.
Projection ( ) Deletes unwanted columns from relation.
Cross-product ( ) Allows us to combine two relations.
Set-difference ( ) Gives tuples in 1st rel., but not in 2nd rel.
Union (  ) Gives tuples in rel. 1 and in rel. 2.
Intersection, join, division, renaming: Not (theoretically)
essential, but (practically) very useful.
Since each operation returns a relation, operations
can be composed! (Algebra is “closed”.)
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Cross-Product
Each row of S1 is paired with each row of R1.
 Result schema has one field per field of S1 and R1,
with field names `inherited’ if possible.
 Conflict: Both S1 and R1 have a field called sid.

(sid) sname rating age
(sid) bid day
22
dustin
7
45.0
22
101 10/10/96
22
dustin
7
45.0
58
103 11/12/96
31
lubber
8
55.5
22
101 10/10/96
31
lubber
8
55.5
58
103 11/12/96
58
rusty
10
35.0
22
101 10/10/96
58
rusty
10
35.0
58
103 11/12/96
 Renaming operator:
 (C(1 sid1, 5  sid 2), S1 R1)
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Joins

Condition Join:
(sid)
22
31
R  c S   c ( R  S)
sname rating age
dustin 7
45.0
lubber 8
55.5
S1 
(sid) bid
58
103
58
103
S1. sid  R1. sid
day
11/12/96
11/12/96
R1
Result schema same as that of cross-product.
 Fewer tuples than cross-product, might be
able to compute more efficiently
 Sometimes called a theta-join.

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Joins (Cont’d)

Equi-Join: A special case of condition join where
the condition c contains only equalities.
sid
sname rating age bid day
22
dustin 7
45.0 101 10/10/96
58
rusty
10
35.0 103 11/12/96
S1 


R1
sid
Result schema similar to cross-product, but only
one copy of fields for which equality is specified.
Natural Join: Equijoin on all fields having the same
name in both relations.
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Relational Calculus

Has two flavors:
 Tuple relational calculus (TRC)
 Domain relational calculus (DRC).

Has variables, constants, comparison ops, logical
connectives, and quantifiers.




TRC: Variables range over (i.e., get bound to) tuples.
DRC: Variables range over domain elements (= field values).
Both TRC and DRC are simple subsets of first-order logic.
Expressions in the calculus are called formulas. An
answer tuple is essentially an assignment of constants
to variables that make the formula evaluate to true.
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Tuple Relational Calculus

Query has the form:










t| p(t)

Answer includes all tuples t that make the formula
p(t) be true.

Formula is recursively defined, starting with
simple atomic formulas (getting tuples from
relations or making comparisons of values),
and building bigger formulas using the logical
connectives.
20
TRC Formulas

Atomic formula:


RRname,
op is one of
or R.a op S.b, or R.a op constant
, , , ,, 
Formula:
 an atomic formula, or
  p, p  q, p  q , where p and q are formulas, or
 R( p(R)), where variable R is free in p(R), or
 R( p(R)) , where variable R is free in p(R)
 The use of quantifiers R and R is said to bind R.


A variable that is not bound is free.
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Overview: Features of SQL

Data definition language: used to create, destroy, and
modify tables and views.
Data manipulation language: used to pose queries, and
to insert, delete, and modify rows.
 Triggers and advanced integrity constraints: used to
specify actions that the DBMS will execute automatically.
 Embeddded SQL: allows SQL to be called from a host

language, or

Dynamic SQL: allows run-time creation and execution of
queries .
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Syntax of Basic SQL Query
SELECT
FROM
WHERE
[DISTINCT] target-list
relation-list
qualification
relation-list A list of relation names (possibly with a
range-variable after each name).
 target-list A list of attributes of relations in relation-list
 qualification Comparisons (Attr op const or Attr1 op
Attr2, where op is one of , ,  , , ,  )
combined using AND, OR and NOT.
 DISTINCT is an optional keyword indicating that the
answer should not contain duplicates.

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Sample Query and Conceptual Evaluation
SELECT S.sname
FROM Sailors S, Reserves R
WHERE S.sid=R.sid AND R.bid=103
(sid) sname rating age
(sid) bid day
22 dustin
7
45.0
22
101 10/10/96
22 dustin
7
45.0
58
103 11/12/96
31 lubber
8
55.5
22
101 10/10/96
31 lubber
8
55.5
58
103 11/12/96
58 rusty
10
35.0
22
101 10/10/96
58 rusty
10
35.0
58
103 11/12/96
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Aggregate Operators

Significant extension of relational
algebra
SELECT COUNT (*)
FROM Sailors S
SELECT AVG (S.age)
FROM Sailors S
WHERE S.rating=10
COUNT (*)
COUNT ( [DISTINCT] A)
SUM ( [DISTINCT] A)
AVG ( [DISTINCT] A)
MAX (A)
MIN (A)
single column
SELECT S.sname
FROM Sailors S
WHERE S.rating= (SELECT MAX(S2.rating)
FROM Sailors S2)
SELECT COUNT (DISTINCT S.rating)
FROM Sailors S
WHERE S.sname=‘Bob’
SELECT AVG ( DISTINCT S.age)
FROM Sailors S
WHERE S.rating=10
25
Queries With GROUP BY and HAVING
SELECT
FROM
WHERE
GROUP BY
HAVING

[DISTINCT] target-list
relation-list
qualification
grouping-list
group-qualification
The target-list contains (i) attribute names (ii) terms
with aggregate operations (e.g., MIN (S.age)).

The attribute list (i) must be a subset of grouping-list.
Intuitively, each answer tuple corresponds to a group, and
these attributes must have a single value per group. (A
group is a set of tuples that have the same value for all
attributes in grouping-list.)
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Find the age of the youngest sailor with age  18,
for each rating with at least 2 such sailors
SELECT S.rating, MIN (S.age)
FROM Sailors S
WHERE S.age >= 18
GROUP BY S.rating
HAVING COUNT (*) > 1


Only S.rating and S.age are
mentioned in the SELECT,
GROUP BY or HAVING clauses;
other attributes `unnecessary’.
2nd column of result is
unnamed. (Use AS to name it.)
sid sname rating age
22 dustin
7
45.0
31 lubber
8
55.5
71 zorba
10 16.0
64 horatio
7
35.0
29 brutus
1
33.0
58 rusty
10 35.0
rating age
1
33.0
7
45.0
rating
7
35.0
7
35.0
8
55.5
Answer relation
10 35.0
27
Summary
SQL
 is more declarative than earlier, procedural query languages.
 is relationally complete; in fact, significantly more expressive
power than relational algebra.




In practice, users need to be aware of how queries are optimized and
evaluated for best results.
has any alternative ways to write a query; optimizer should
look for most efficient evaluation plan.
allows specification of rich integrity constraints.
Provides triggers to respond to changes in the database.
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