Introduction to
Relational Databases
and SQL
Brian Panulla
Web 2004 Pre-Conference
Administrivia
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Objectives
Agenda
Prerequisites
Schedule
Logistics
Presenter Introduction
Attendee Introductions
Objectives
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The primary objective of this session is to
teach the basics of working with common
Relational Databases, including data
modeling and database design techniques.

Participants will also receive an introduction
to query building using Standard or
Structured Query Language (SQL).
Objectives

To that end, upon completion of the session you
should be able to:
 Understand
database concepts and terminology
 Design and create tables
 Use SQL to retrieve and analyze information
 Use SQL to enter and manipulate data
Agenda
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Lesson 1: Database Concepts
Lesson 2: Entity-Relationship Modeling
Lesson 3: Creating Tables
Lesson 4: Using SELECT queries
Lesson 5: Using INSERT queries
Lesson 6: Using UPDATE queries
Lesson 7: Using Delete queries
Lesson 8: Database Project
Prerequisites
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
General experience in Web site development
or management
General experience in a scripting language
(ColdFusion, PHP, ASP) or full programming
language (C/C++, Java)
Schedule
(Half-Day Session)
Start
1:00 PM
Break (approximate)
3:00 PM
Adjourn
5:00 pm
Logistics
• Restrooms
• Drinking fountains,
refreshments, snacks
• Laptops
• Messages/phones
• Security
• Emergency measures
Presenter
Brian Panulla
B.S. Science, PSU (2000)
Chief Information Officer
Campus Data Group, LLC
E-mail:
bpanulla@psu.edu
Attendees
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Your name
Organization name
Current position
Background in databases
Expectations
Questions?!?!
Ask
away!
Lesson 1:
Database Concepts
Lesson 1 Objectives
A.
B.
Discuss database concepts and
terminology
Learn database design principles
Database Terminology
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Database
Table (or relation, entity)
Row (or record, tuple)
Column (or field, attribute)
Data value
What is a database?


Common databases
Database tasks:







Retrieving
Sorting
Summarizing
Inserting
Updating
Deleting
Common DB software
What is a table?


Tables are the fundamental component of
any relational database.
A table stores the data corresponding to a
specific type of object

For example:
A Students table would store student information
 An Orders table would store customer order
information

What is a table?


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Tables are made up of rows and columns
The columns of a table describe the
characteristics of the information stored in
that table
The data in each row belongs to a given
instance of the type of data stored in a
particular table
What is a table?


Each row contains one data value per
column.
The range of values that can go into a
particular column of a row is called the
domain of that column, and is generally
restricted to data of a specific type (integers,
character data, dates, etc.)
Sample Database Design
Name
Office Address
Locality
Phone
Department
Bob Walker
212 S. Allen Street
State College, PA 16801 (814) 555-1111 Sales
Beth Adams
5251 Electric Avenue
Lewistown, PA 17044
(814) 555-0165 Engineering
Maggie Taylor 227 S. Allen Street
State College, PA 16803 (814) 555-0771 Marketing
Matt Peterson 212 S. Allen Street
State College, PA 16803 (814) 555-1111 Sales
What is a
“Relational Database?”

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The term “relational database” comes from
the mathematical definition of a relation, or
set. All objects in a relation must have the
same properties or characteristics
The point? Tables group similar data or
objects, with use one table per set of objects
Q: Is Excel a relational database system?
Database Design


An art unto itself, database design skills are
crucial in the development of efficient, stable
Web applications
Planning your database design should
generally be one of the first tasks in a
development project. Too often, however, the
database is developed “as we go along,”
creating problem after problem
Lesson 2:
Entity-Relationship
Modeling
Entity – Relationship Modeling



Entity-Relationship (or E-R) Modeling is the
name given to one particular method of
relational database design
The data to be stored in a database is
categorized into entities. An entity is an
example of one type of object to be modeled
by the database.
Entity names are typically nouns.
Some Basic Entities
Student
Course
Professor
Exercise

Discuss some of the entities that would exist
in databases for:
 the
participants in a youth sporting league
(baseball, softball, soccer, etc.)
 the
inventory of an independent gift shop or
other store
E-R Modeling: Attributes

Each entity has one or more attributes that
further describe that entity or relationship.
An Entity with Attributes
Name
Phone
ID
Students
Email
GPA
Exercise

Discuss some of the attributes that would
describe the following entities:
 the
Teams entity in the youth sporting league
database
 the
Products entity in the gift shop inventory
database
E-R Modeling: Relationships

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A given entity can be correlated with other
entities by way of relationships
A relationship is typically named with a verb
phrase:
 A Person
is a Student
 A Book is published by a Publisher

There may also be attributes that truly belong
to the relationship and not to an entity
Relationships
Name
Phone
ID
Professor
Email
GPA
Student
Year
Teaches
Date
Enrolls
In
Grade
Course
Exercise

Devise the relationships that connect the
entities in the databases discussed in one of
the previous exercises:
 The
youth sporting league database
 The
gift shop inventory database
Lesson 3:
Creating Tables
Lesson 3 Objectives
A.
B.
C.
Convert an E-R Diagram to a relational
schema
Identify improvements to the design tables
Use the CREATE TABLE SQL statement to
create tables in a database
Creating Tables
With an E-R Model in hand, the actual
creation of a database is very
straightforward
 Each entity will generally become a table
 Any relationships with one or more
attributes will also become a table
 Relationships without attributes may
sometimes be modeled as a table

Ground Rules for Relational
Database Design
1)
2)
3)
4)
No multi-part or multi-value fields
Eliminate redundant information
Avoid designs that call for the addition of
columns to store new data
Avoid “anomalies”:
a)
b)
c)
Update
Delete
Insert
Sample Database Design
Name
Office Address
Locality
Phone
Department
Bob Walker
212 S. Allen Street
State College, PA 16801 (814) 555-1111 Sales
Beth Adams
5251 Electric Avenue
Lewistown, PA 17044
(814) 555-0165 Engineering
Maggie Taylor 227 S. Allen Street
State College, PA 16803 (814) 555-0771 Marketing
Matt Peterson 212 S. Allen Street
State College, PA 16803 (814) 555-1111 Sales
Sample Database Design
1)
No multi-part or multi-value fields
Name
Office Address
Locality
Phone
Department
Bob Walker
212 S. Allen Street
State College, PA 16801 (814) 555-1111 Sales
Beth Adams
5251 Electric Avenue
Lewistown, PA 17044
(814) 555-0165 Engineering
Maggie Taylor 227 S. Allen Street
State College, PA 16803 (814) 555-0771 Marketing
Matt Peterson 212 S. Allen Street
State College, PA 16803 (814) 555-1111 Sales
Sample Database Design
2)
Eliminate redundant information
Name
Office Address
Locality
Phone
Department
Bob Walker
212 S. Allen Street
State College, PA 16801 (814) 555-1111 Sales
Beth Adams
5251 Electric Avenue
Lewistown, PA 17044
(814) 555-0165 Engineering
Maggie Taylor 227 S. Allen Street
State College, PA 16803 (814) 555-0771 Marketing
Matt Peterson 212 S. Allen Street
State College, PA 16803 (814) 555-1111 Sales
Sample Database Design
3)
Avoid designs that call for the addition of
columns to store new data
Name
Office Address
Locality
Phone
Department 1
Bob Walker
212 S. Allen Street
State College, PA 16801 (814) 555-1111 Sales
Beth Adams
5251 Electric Avenue
Lewistown, PA 17044
(814) 555-0165 Engineering
Maggie Taylor 227 S. Allen Street
State College, PA 16803 (814) 555-0771 Marketing
Matt Peterson 212 S. Allen Street
State College, PA 16803 (814) 555-1111 Sales
Department 2
Marketing
Sample Database Design
4)
Avoid anomalies: Update Anomaly
Name
Office Address
Locality
Phone
Department
Bob Walker
212 S. Allen Street
State College, PA 16801 (814) 555-1111 Sales
Beth Adams
5251 Electric Avenue
Lewistown, PA 17044
(814) 555-0165 Engineering
Maggie Taylor 227 S. Allen Street
State College, PA 16803 (814) 555-0771 Marketing
Matt Peterson 212 S. Allen Street
State College, PA 16803 (814) 555-1111 Sales
Sample Database Design
Delete Anomaly
Name
Office Address
Locality
Phone
Department
Bob Walker
212 S. Allen Street
State College, PA 16801 (814) 555-1111 Sales
Beth Adams
5251 Electric Avenue
Lewistown, PA 17044
(814) 555-0165 Engineering
Maggie Taylor 227 S. Allen Street
State College, PA 16803 (814) 555-0771 Marketing
Matt Peterson 212 S. Allen Street
State College, PA 16803 (814) 555-1111 Sales
Sample Database Design
Insert Anomaly
Name
Office Address
Locality
Phone
Department
Bob Walker
212 S. Allen Street
State College, PA 16801 (814) 555-1111 Sales
Beth Adams
5251 Electric Avenue
Lewistown, PA 17044
(814) 555-0165 Engineering
Maggie Taylor 227 S. Allen Street
State College, PA 16803 (814) 555-0771 Marketing
Matt Peterson 212 S. Allen Street
State College, PA 16803 (814) 555-1111 Sales
Manufacturing
Keys

A key is a field or set of fields that can be
used to uniquely identify a particular record in
a database table.

Examples:
Name, Department
 SSN


A Primary Key is a field or set of fields
chosen by the database designer to be used
to define relationships between tables
Primary Keys

Primary Key fields:



Must contain unique, non-duplicated values (or sets of values in
the case of multi-field keys
Cannot be NULL
In the event that no one column in a table is
an appropriate Primary Key, an artificial
primary key column is usually generated
Foreign Keys


When the values of a Primary Key column in
a table are shared by a common column in a
child table, that column is called the Foreign
Key of the relationship.
Foreign Key fields must have the same data
type and size as their related Primary Key
field.
Foreign Keys


Foreign Keys are used in other tables in
order to maintain relationships between data
values.
By allowing us to split data into multiple
related tables, foreign keys can help
eliminate the anomalies described previously
A Better Design
EID Last Name
1
2
3
4
Walker
Adams
Taylor
Peterson
OID
EID
First Name OID
Bob
Beth
Maggie
Matt
Office Address
1
2
3
1
Locality
DID
1
1
2
2
3
3
4
1
1
3
State
Zip
DID
Department
1
2
3
4
Sales
Engineering
Marketing
Manufacturing
Phone
1 212 S. Allen Street
State College
PA
16801 (814) 555-1111
2 5251 Electric Avenue
Lewistown
PA
17044 (814) 555-0165
3 227 S. Allen Street
State College
PA
16801 (814) 555-0771
The Relational Schema
Employees
Offices
EID
OID
OID
Address
FirstName
Locality
Lastname
State
Zip
Phone
Departments
DeptAssignments
DID
EID
DeptName
DID
Exercise

Develop a relational schema for one of the
databases from the previous lesson
An Introduction to
Relationships
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Cardinality
One-to-Many Relationships
One-to-One Relationships
Many-to-Many Relationships
Cardinality

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Cardinality is the term used to describe the
character of a relationship
Three types of cardinality exist:



One to Many
Many to Many
One to One
One-to-Many Relationships
EID Last Name
1
2
3
4
Walker
Adams
Taylor
Peterson
First Name OID
Bob
Beth
Maggie
Matt
OID
1
2
3
1
Office Address
Each row in Employees relates to
only one row in Offices, while
each row in Offices may relate to
many rows in Employees
Locality
State
Zip
Phone
1 212 S. Allen Street
State College
PA
16801 (814) 555-1111
2 5251 Electric Avenue
Lewistown
PA
17044 (814) 555-0165
3 227 S. Allen Street
State College
PA
16801 (814) 555-0771
Many-to-Many Relationships
EID Last Name
1
2
3
4
Walker
Adams
Taylor
Peterson
First Name OID
Bob
Beth
Maggie
Matt
1
2
3
1
DID
Department
1
2
3
4
Sales
Engineering
Marketing
Manufacturing
Each row in Employees may
relates many rows in Departments,
and vice versa.
Many-to-Many Relationships
EID Last Name
1
2
3
4
Walker
Adams
Taylor
Peterson
First Name OID
Bob
Beth
Maggie
Matt
1
2
3
1
EID
DID
1
1
2
2
3
3
4
1
1
3
DID
Department
1
2
3
4
Sales
Engineering
Marketing
Manufacturing
A Many-to-Many Relationship can usually be
modeled by adding an additional “join table,”
creating two One-to-Many Relationships instead.
One-to-One Relationships
EID Last Name
1
2
3
4
Walker
Adams
Taylor
Peterson
First Name OID
Bob
Beth
Maggie
Matt
1
2
3
1
EID
1
2
3
4
Salary
$35,200
$75,000
$45,000
$22,000
A One-to-One Relationship is usually created
for security and/or performance reasons.
Technically, the two tables involved in a Oneto-One relationship could be combined into one
table with no loss of data
Exercise

Describe the cardinality of the relationships in
your relational schema
Data Types



Each column of a table must have a specified
data type supported by the database
software.
The database will enforce the requirement
that data entered into a field must comply
with the field’s data type
Choose the smallest data type appropriate for
each column, leaving room for future
expansion (No Y2K-like bugs!)
Data Types
• INTEGER
• FLOAT (floating
point numbers)
• NUMERIC (real
numbers)
•
•
•
•
•
CHAR
VARCHAR
TEXT
TIMESTAMP
DATE
Creating Tables

Choose good field and table names
 No
spaces or special characters (other than
the underscore “_” )
 Use meaningful names
 The
Leszynski Naming Convention (or LNC)
is a scheme for helping you create more
meaningful table and field names.
LNC Naming

Originally suggested by Stan Leszynski and
Greg Reddick in a paper entitled "The
Leszinski/Reddick Guidelines for Access 1.x,
2.x" it has become the convention used by
many developers.
LNC Naming

This naming convention suggests that you
precede object names with three letter "tags"
to help developers work with unfamiliar
database objects.
LNC Prefixes
Data Type:
• n (integer or numeric)
• f (float)
• c (char)
• vc (varchar)
• dt (datetime or
timestamp
Role:
• pk (primary key)
• fk (foreign key)
and:
• tbl (table)
• vw (view)
• sp (stored procedure)
A Sample Database
tblAccounts
npkAccountID
nfkInstitutionID
dtCreated
SERIAL
INTEGER
TIMESTAMP
not null
not null
not null
[0,n]
npkAccountID = nfkAccountID
npkInstitutionID = nfkInstitutionID
[0,n]
npkUserID
nfkAccountID
vcUsername
vcPassword
vcNameFirst
vcNameLast
tblUsers
SERIAL
INTEGER
VARCHAR(24)
VARCHAR(32)
VARCHAR(24)
VARCHAR(24)
not null
not null
not null
not null
null
null
tblInstitutions
npkInstitutionID
SERIAL
vcFullName
VARCHAR(255)
vcSortName
VARCHAR(255)
vcCity
VARCHAR(255)
cfkState
CHAR(2)
not null
not null
not null
null
not null
CREATE TABLE

You can build a table in a SQL-compatible database with the
CREATE TABLE statement
create table Offices (
OfficeID integer primary key,
Address varchar(32),
Locality varchar(32),
State char(2),
Zip varchar(10),
Phone varchar(12)
)
NULL Values


Relational databases have a special value, called
the NULL, that can be used as a placeholder when
information is unknown, missing, or to be filled in
later.
NULL values are different than



Zero
Empty Strings
A column of any data type can be NULL, though
PRIMARY KEY columns can never be NULL
Default values



Each column in a table may have a default
value specified.
The database will set the column to that
value if no value is passed during insert
If no default value is specified, the column will
be set to NULL if possible.
CREATE TABLE with defaults

Our CREATE TABLE from the previous step with NULL
preferences and defaults:
create table Offices (
OfficeID integer NOT NULL primary key,
Address varchar(32) NULL,
Locality varchar(32) NULL DEFAULT ‘State College’,
State char(2) NOT NULL DEFAULT ‘PA’,
Zip varchar(10) NULL,
Phone varchar(12) NULL
)
Lesson 3 Review


Why is the planning step important when
creating a database?
Why is separating data into multiple tables a
good idea?
Exercise

Practice making tables in your database by
building on the schema you developed in
previous exercises.
Lesson 4:
SQL: Retrieving
Data
SQL


SQL (Structured or Standard Query
Language) is defined by several ANSI
standards
Despite being “standardized,” every DB
platform may implement some of the
language syntax differently
SQL: Select

A SELECT query is the most common type of SQL
query:
SELECT * FROM employees

This query will retrieve all the columns of all of the
rows from the table employees.
SQL: Select

To only retrieve only some of the columns of
a table, list them after SELECT:
SELECT empID, lastname, firstname
FROM employees
SQL: Select

A WHERE clause may be added to selectively
retrieve only those rows in a table that match a
given constraint:
SELECT * FROM employees
WHERE lastname = ‘smith’

Only employee rows matching the last name of
“smith” will be returned. Normal equality or inequality
operators (< > =) may be used.
SQL: Select

Multiple conditions may be added to the
WHERE clause, using AND, OR, and NOT as
operators and parentheses as appropriate:
SELECT * FROM employees
WHERE department=‘sales’ AND
(lastname = ‘smith’ OR lastname=‘jones’)
Exercise
Using
a WHERE clause
SQL: Select

Add an ORDER BY clause to sort the records:
SELECT empID, lastname, firstname
FROM employees
WHERE department = “sales”
ORDER BY lastname

The keyword DESC can be used to order from
highest to lowest values
Exercise
Sorting
a Query
Joins


Table joins alIow the database developer to combine
information from related tables into a single,
coherent record set and return those records to the
program
While the standard type of join, the inner join, can
often provide the information needed, additional join
types are available to control how the database
combines the data in two tables
Inner Joins

Inner Joins are the typical join types
encountered in SQL. In a two table inner join,
fields from records from the first table that
can be correlated to records in the second
table appear “side-by-side” with fields from
the second table in the resulting record set
Join Types
ID
1
2
3
4
Name
Brian
Karen
Mary
Dave
DeptID
1
2
3
4
Dept
1
2
3
1
DeptName
Engineering
Education
Marketing
Sales
Diagramming Joins
Cartesian Product
ID
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
Name
Brian
Brian
Brian
Brian
Karen
Karen
Karen
Karen
Mary
Mary
Mary
Mary
Dave
Dave
Dave
Dave
Dept
1
1
1
1
2
2
2
2
3
3
3
3
1
1
1
1
DeptID
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
DeptName
Engineering
Education
Marketing
Sales
Engineering
Education
Marketing
Sales
Engineering
Education
Marketing
Sales
Engineering
Education
Marketing
Sales
Inner Joins
Inner Join
ID
1
2
3
4
Name
Brian
Karen
Mary
Dave
Dept
1
2
3
1
DeptID
1
2
3
1
DeptName
Engineering
Education
Marketing
Engineering
Inner Joins


Two versions of the join syntax exist, corresponding
to two different versions of the ANSI SQL Standard.
Traditional syntax:
SELECT * FROM
tblAccounts A, tblUsers U
WHERE A.npkAccountID = U.nfkAccountID
AND dtCreated > ‘1/1/2003’

Instructions on how to join the tables listed in the
FROM clause are placed in the WHERE clause
Inner Joins

The newer ANSI SQL 92 syntax makes it easier to
write other join types:
SELECT * FROM
tblAccounts A INNER JOIN tblUsers U
ON A.npkAccountID = U.nfkAccountID
WHERE dtCreated > ‘1/1/2003’

Instructions on how to join the tables listed in the
FROM clause are placed in a new ON clause,
leaving the WHERE clause for filtering records
Inner Join syntax comparison
SELECT * FROM
tblAccounts A, tblUsers U
WHERE A.npkAccountID = U.nfkAccountID
AND dtCreated > ‘1/1/2003’
SELECT * FROM
tblAccounts A INNER JOIN tblUsers U
ON A.npkAccountID = U.nfkAccountID
WHERE dtCreated > ‘1/1/2003’
Inner Joins - more tables
SELECT * FROM
(tblAccounts A INNER JOIN tblUsers U
ON A.npkAccountID = U.nfkAccountID)
INNER JOIN tblInstitutions I
ON A.nfkInstitutionID = I.npkInstiutionID
WHERE dtCreated > ‘1/1/2003’
Part I:
Advanced SQL
Outer Joins



Outer Joins are an extremely powerful query feature
available in SQL. Outer joins are supported in nearly
all major RDBMSs
In an outer join, all records from one table will be
included in the resulting recordset, along with any
records in the second table that can be correlated to
records in the first table.
As with inner joins, fields from both tables appear
“side-by-side” in the resulting record set, but fields
from records that have no counterpart in the second
table receive NULL values.
Outer Joins


Outer joins are directional. An outer join may either
be a left outer join or a right outer join.
By specifying the direction of the join, you tell your
database from which table it should include all
records:
SELECT * FROM
tblInstitutions I LEFT OUTER JOIN tblAccounts A
ON I.npkInstitutionID = A.nfkInstitutionID
will include all records from the table tblInstitutions.
Outer Join
Outer Join
ID
1
2
3
4
NULL
Name
Brian
Karen
Mary
Dave
NULL
Dept
1
2
3
1
NULL
DeptID
1
2
3
1
4
DeptName
Engineering
Education
Marketing
Engineering
Sales
Outer Joins

Often, the table pointed to by the direction of an
outer join will be the parent table in a relationship.
tblAccounts A LEFT OUTER JOIN tblUsers U
makes much more sense than:
tblUsers U LEFT OUTER JOIN tblAccounts A
as tblUsers shouldn’t contain any rows that don’t
relate to an Account, but an Account may have no
users defined.
Aggregate Functions

Nearly all RDBMSs have support for basic
aggregate functions (also called set
functions) - functions that operate on a set
of values and return a single value.

Examples:




Count(*)
Sum(*)
Max(*)
Min(*)
Aggregate functions collapse your recordset
into a single record

For example:
SELECT count(*) as RecordCount,
max(nRevenue) as MaxRevenue,
min(nRevenue) as MinRevenue,
avg(nRevenue) as AvgRevenue
FROM tblModuleData_FoodSvc
New tables
npkDemogID = nfkDemogID
tblDemographics
npkDemogID
SERIAL
nfkAccountID
INTEGER
nfkAcadYearID
INTEGER
nFTEEnrollment
INTEGER
dtUpdated
TIMESTAMP
tblModuleData_FoodSvc
nfkDemogID
INTEGER
nMealPlanEnrollment
INTEGER
nPotentialMeals
INTEGER
nActualMeals
INTEGER
nTotalMeals
INTEGER
fMealPlanSales
NUMERIC(15,2)
fCateringSales
NUMERIC(15,2)
fConferenceSales
NUMERIC(15,2)
fRevenue
NUMERIC(15,2)
fFoodBevCosts
NUMERIC(15,2)
fLaborHours
FLOAT
fSalariesWages
NUMERIC(15,2)
fLaborCosts
NUMERIC(15,2)
fNonFoodCosts
NUMERIC(15,2)
fDirectCosts
NUMERIC(15,2)
fOtherCosts
NUMERIC(15,2)
nfkMgmtArrangeID
SMALLINT
nfkMgmtFirmID
INTEGER
[0,n]
not null
not null
not null
not null
null
[0,n]
not null
not null
not null
not null
not null
not null
not null
not null
not null
not null
not null
not null
not null
not null
not null
not null
not null
null
npkAccountID = nfkAccountID
npkAccountID
nfkAccountRepID
nfkInstitutionID
dtCreated
tblAccounts
SERIAL
not null
[0,n]
INTEGER
not null
INTEGER npkAccountRepID
not null
TIMESTAMP not null
=
tblAccountReps
npkAccountRepID
SERIAL
not null
vcNameFirst
VARCHAR(24) null
nfkAccountRepID
vcNameLast
VARCHAR(24) null
Grouping Records

You may split your recordset into two or more
groups according to the values of one of the
columns in your query:
SELECT nfkAccountID,
count(*) as RecordCount,
max(nRevenue) as MaxRevenue,
min(nRevenue) as MinRevenue,
avg(nRevenue) as AvgRevenue
FROM tblModuleData_FoodSvc M INNER JOIN tblDemographics D
ON M.nfkDemogID = D.npkDemogID
GROUP BY nfkAccountID
Subqueries

Many RDBMSs allow you to nest queries into
a single operation.

The two most common subquery examples
are:


“Virtual Tables”
“IN-clause” subqueries
Subqueries

“Virtual table” subqueries allow you to use the result
of a query as a temporary table in a second query:
SELECT * FROM tblAccounts A INNER JOIN
(SELECT nfkAccountID, count(*)
FROM tblUsers U
GROUP BY nfkAccountID) U ON
A.npkAccountID = U.nfkAccountID
this can be used to reduce the number of fields in
your GROUP BY statement in the outer query
Subqueries

“IN clause” subqueries allow you filter your records
according to the values obtained by a subquery
SELECT * FROM tblUsers U
WHERE nfkAccountID IN
(SELECT npkAccountID
FROM tblAccounts
WHERE count(*) > 2
GROUP BY nfkAccountRepID)
Subquery Example



EduSoft’s portal Web site is meant to be used by
multiple individuals at a given institution.
If multiple users haven’t been created within three
months of account creation, chances are the main
user at the institution is not making good use of the
account and may need help.
Find all Accounts opened before 1/1/03 for which
only one user login has been established, and pass
that information onto our Customer Service manager
Subquery Example
SELECT npkAccountID, dtCreated
FROM tblAccounts
WHERE npkAccountID IN
(SELECT nfkAccountID FROM tblUsers
WHERE count(*) = 1
GROUP BY nfkAccountID)
AND dtCreated < ‘1/1/2003’
Stored Procedures


Stored Procedures allow you to pre-compile
complicated queries on the database server
Most RDBMSs that support SPs provide you
with a fairly functional programming language
in which to write them:


Transact-SQL (T-SQL) is MS SQL Server
PL/SQL in Oracle
Stored Procedures
• On the server
side, the SP is
written in your
RDBMSs dialect
of SQL
• This SP is from
MS SQL Server 7
Stored Procedures

SPs are excellent components for two main
reasons:



Database logic encapsulation - have all queries coded into SPs
by your DBA, and made available to developers
Performance - the SQL does not need to be interpreted and
compiled on each execution, saving processor time
Big uses for SPs include Ad Banner or click tracking,
autonumber tables for DBMSs without sequences