Chapter 3B Objectives Relational Set Operators · Chapter 3B Objectives • Learn About – relational database operators • SELECT & DIFFERENCE • PROJECT & JOIN ... Relationships

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Chapter 3B Objectives

• Learn About – relational database operators

• SELECT & DIFFERENCE

• PROJECT & JOIN

• UNION

• PRODUCT

• INTERSECT

• DIVIDE

– The Database Meta Objects• the data dictionary

• the system catalog

– Importance of indexing is

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Relational Set Operators

Relational algebra

• Defines theoretical way of manipulating table

contents using relational operators

• Use of relational algebra operators on existing

relations produces new relations.

• The degree of relation completeness can be

defined by the extent to which relational algebra is

supported

• Is the theory behind what makes relational tables,

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Relational Set Operators

• SQL language uses the Select statement to do all

these Set Operations, if supported by the DBMS

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• SELECT • DIFFERENCE

• PROJECT • PRODUCT

• UNION • DIVIDE

• INTERSECT • JOIN

Relational Algebra Operations

• Select

– Yields values for all rows found in a table

– Can be used to list either all row values or it can

yield only those row values that match a specified

criterion

– Yields a horizontal subset of a table

• Project

– Yields all values for selected attributes

– Yields a vertical subset of a table

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Select

• SELECT yields values for the attributes found in a

table… a horizontal subset of the table.

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Project

• PROJECT produces a list of all values for selected

attributes. It yields a vertical subset of a table.

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Relational Algebra Operations

• UNION:

– Combines all rows from two tables, excluding

duplicate rows

– Tables must be union compatible, that is, have the

same attribute characteristics.

• INTERSECT:

– Creates a listing that contains only the rows that

appear in both tables.

– The two tables must be union compatible.

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Union & Intersect

• UNION combines all rows from two union

compatible tables.

• Intersect yields only the rows that appear in both

tables

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Relational Algebra Operations

• Difference

– Yields all rows in one table not found in the other

table—that is, it subtracts one table from the other

– The tables must be union compatible

• Product

– Yields all possible pairs of rows from two tables, that is

the # of rows in T1 times the # of rows in T2. Also

known as the Cartesian product

• Divide

– A larger table is ‘divided’ by a smaller, single attribute

table and extracts rows where the values are equal.

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Difference & Product

• DIFFERENCE yields all rows in one table that are

not found in the other table.

• PRODUCT produces a list of all possible pairs of

rows from two tables.

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Divide

• DIVIDE: The larger table is ‘divided’ by smaller

table. In this case where A & B are found and the

2nd column values are equal then that value is

extracted to the new table. Divide requires the

use of a single-column table as the divisor.

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Join

• JOIN

– Allows us to combine information from two or

more tables

– Real power behind the relational database,

allowing the use of independent tables linked by

common attributes

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Natural Join

• Links tables by selecting only rows with common

values in their common attribute(s)

• The final result comes from a three-stage

process:

• PRODUCT of the tables is created

• SELECT is performed on resulting Product output

to yield only the rows for join columns are equal

[Common column(s) are called join column(s)]

• PROJECT is performed on the Select results to yield

a single copy of each attribute eliminating join

duplicate columns

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Natural Join

• Two Tables That Will Be Used

in Join Illustrations

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Natural Join, Step 1

• Product of the two tables

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Natural Join, Step 2

• Select - yielding only rows with equal Agent_Code

values

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Natural Join, Step 3

• Project - eliminates the duplicate Agent_Code

column.

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Natural Join

• Final outcome yields a new table that

– Does not include unmatched rows

– Provides only copies of matches

• The column on which we made the JOIN—that is,

AGENT_CODE, occurs only once in the new table.

• If the same AGENT_CODE were to occur several

times in the AGENT table,

– a customer would be listed for each match

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Other Join Types

• Equijoin

– Links tables on the basis of an equality condition that compares specified columns

– Does not eliminate duplicate (join) columns

– Join criterion must be explicitly defined

– Takes its name from the equality comparison operator (=) used in the condition.

• Theta join

– Any other comparison operator is used (<, >, not =)

• Outer join

– Matched pairs are retained, and any unmatched values in other table are left null

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Outer Join

• Left outer join :

– Yields all rows in

CUSTOMER table,

including those that do

not have a matching

value in the AGENT

table

• Right outer join:

– Yields all rows in

AGENT table, including

those that do not have

matching values in the

CUSTOMER table20CS275 Fall 2010

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Relational DBMS Classification

Classification

• Relationally complete

• Fully Relational

• Minimally relational

• Tabular

Features

Supports all 8 functions & enforces both entity & referential integrity rules

Supports all eight functions but not integrity rules

Supports only SELECT, PROJECT, and JOIN

Supports only SELECT, PROJECT, and JOIN, and requires that all access paths be defined by the user.

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Other (Metadata) DB Objects

• Data dictionary– Provides information on all tables within database

– Attribute names and characteristics

– Provides detailed accounting of all tables found within the user/designer-created database

– Contains metadata—data about data

• System catalog tables– System-created detailed data dictionary

– Stores database characteristics and contents

– Tables can be queried just like any other tables

– Automatically produces database documentation

• Synonym table– Contains secondary names for objects

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A Sample Data Dictionary

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Other (Metadata) DB Objects

• Index Tables

– Special table which points to another table

– Makes retrieval of data faster

– Part of the physical, not logical design

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Other (Metadata) DB Objects

• An Index table enables an orderly(sorted)

arrangement to logically access rows in a table

• Index key

– Index’s reference point

– Points to data location /row identified by the key

• Unique index

– Index in which the index key can have only one

pointer value (row) associated with it

• Each index table is associated with only one

table

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Relationships within the Relational

Database

• 1:M relationship – Relational modeling ideal

– Should be the norm in any relational database

design

• 1:1 relationship– Should be rare in any relational database design

• M:N relationships– Cannot be implemented as such in the relational

model

– M:N relationships can be changed into 1:M

relationships

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The 1:M Relationship

• Relational database norm

• Found in any database environment

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The Implemented 1:M Relationship

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The 1:1 Relationship

• One entity related to only one other entity, and vice versa

• Sometimes means that entity components were not defined properly

– Could indicate that two entities actually belong in the same table

– Certain conditions absolutely require their use

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The Implemented 1:1 Relationship

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The M:N Relationship

• Implemented by breaking it up to produce a set

of 1:M relationships

• Avoid problems inherent to M:N relationship by

creating a composite entity

– Includes as foreign keys the primary keys of tables

to be linked

– usually combined as it’s composite, primary key

– Additional attributes may be assigned as needed

– the composite entity usually contains multiple

occurrences of the foreign key values

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Original M:N relationship

Converted two 1:M relationships

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Converting the M:N relationship into

two 1:M relationships

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Ch03_TinyCollege Database

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Invoicing System

• Another example of creating a 1:M bridge table

for the M:N relationship between Invoice and

Product

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Codd’s Relational Database Rules

• In 1985, Codd published a list of 12 rules to

define a relational database system

– Products marketed as “relational” that did not

meet minimum relational standards

• Even dominant database vendors do not fully

support all 12 rules

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Summary

• The relational model supports relational algebra

functions

– SELECT, PROJECT, JOIN, INTERSECT UNION,

DIFFERENCE, PRODUCT, DIVIDE

• Meta Objects in a database often include: Indexes,

a System Catalog, a Synonym table

• Good design begins by identifying entities,

attributes, and relationships

– 1:1, 1:M, M:N

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