Sql a practical introduction

CONTENTS

1 INTRODUCTION

1.1 A Collection of Related Data : Databases and Database Management
Systems.

1.2 The Database as a Collection of Tables: Relational Databases and SQL.
1.2.1 Tables, Columns and Rows.
1.2.2 The Primary Key.

1.3 Communicating to the DBMS What You Want it to do: Introduction to the
SQL language.

1.4 A Research Project Conducted by IBM: The History of SQL.

1.5 SQL Commands Build Upon Themselves: Organization of This Book.
1.5.1 Notational Conventions.

2 A ROUGH GUIDE TO SQL.

2.1 Consider the Simple Address Book: A Basic Relational Database.

2.2 SQL Commands Fall into Different Categories: Subdivisions of SQL.

2.3 Enter SQL Statements at the Command Line: Using interpretive SQL.

2.4 Use the CREATE TABLE statement: Creating Database Tables.

2.5 Use the INSERT INTO Statement: Adding Data to Tables.

2.6 Use the SELECT Statement: Extracting data from tables.

2.7 Use the UPDATE and DELETE Statements: Modifying data.

2.8 Another Kind of Table, Called a Virtual Table: Views.

2.9 Prevent Access to Sensitive Information: Database security.

3 CREATING AND MAINTAINING TABLES.

3.1 The ANSI Standard Makes Such a Distinction...: The DDL and the ANSI/ISO
standard.

3.2 Single and Multiple Database Architectures: The structure of SQL databases.

3.3 Creating a Database Table: The CREATE TABLE command.

3.4 Apply Restrictions to Groups of Columns: Column and table modifiers.
3.4.1 The NOT NULL modifier.
3.4.2 The UNIQUE modifier.
3.4.3 The INDEX modifier.
3.4.4 The PRIMARY KEY modifier.
3.4.5 The FOREIGN KEY modifier.
3.4.6 The DEFAULT modifier.
3.4.7 The CHECK modifier

3.5 Indexes are Ordered for Extremely Fast Searches: Indexes.

3.6 Changing the Structure of a Table: The ALTER TABLE Command.

3.7 Remove Redundant Tables from the Database: The DROP TABLE
Command.

4 QUERYING SQL TABLES.

4.1 The most basic query: The Simple SELECT statement.
4.1.1 Calculated columns.

4.2 Selecting rows for output: The WHERE clause.
4.2.1 Comparison Test Operators: =, <, <=, >, >=, <>.
4.2.2 Range Test Operator: BETWEEN.
4.2.3 Set Membership Test Operator: IN.
4.2.4 Pattern Matching Test Operator: LIKE.
4.2.5 NULL Value Test Operator: IS NULL.
4.2.6 Logical Operators: AND, OR and NOT.

4.3 Ordering the output of a query: The ORDER BY clause.

4.4 Summary of data in tables: The ANSI aggregate functions.
4.4.1 The number of values or rows: The COUNT() function.
4.4.2 The total of values: The SUM() function.
4.4.3 The average value: The AVG() function.
4.4.4 The minimum and maximum values: The MIN() and MAX() functions.

4.4.5 Sub-totals of grouped values: The GROUP BY clause.
4.4.6 Eliminating groups of data: The HAVING clause.

4.5 Retrieving data from multiple tables: SQL joins.
4.5.1 Classification of joins.

4.6 Joining a table to itself: The self-join.

4.7 Nested SELECT statements: The subquery.

4.8 Linked SELECT statements: The correlated subquery.

4.9 Does the subquery retrieve values: The EXISTS operator.

4.10 Two more subquery operators: The ANY and ALL operators.

4.11 Combining multiple queries: The UNION clause.

5 ADDING AND UPDATING DATA.

5.1 Adding Single Rows at a Time: INSERT command.

5.2 Adding Multiple Rows at a Time: The INSERT with SELECT command.

5.3 Modifying Data in Rows: The UPDATE command.

5.4 Removing Rows Form Tables: The DELETE command.

6 DATA INTEGRITY

6.1 Keeping the Data Tidy: The Basics of Data Integrity.

6.2 Fields That Must Have Values: Non-NULL Columns.

6.3 Values Must be the Right Values: Data Validity.

6.4 Primary key values must be unique: Entity Integrity.

6.5 All Child Rows must have parents: Referential Integrity.

6.6 Integrity Requirements of the User: SQL Triggers.

7 VIEWS

7.1 Restrict the Data You Can See: What is a view?

7.2 How To Make Views: The CREATE VIEW command.

7.3 Looking Through the Window: Using Views.

7.4 Changing Data Through Views: Updating Views.

7.5 Verifying Data Changes: The WITH CHECK Option.

7.6 Shutting the Window: The DROP VIEW Command.

8 DATABASE SECURITY

8.1 The Term Security is Defined as Protection: SQL Privileges.

8.2 Users Must Introduce Themselves: The Logon Procedure.

8.3 The Library Database: An Example System.

8.4 How Privileges Are Passed: The GRANT Statement.
8.4.1 Using views to limit access to columns.
8.4.2 The ALL PRIVILEGES and PUBLIC keywords.
8.4.3 Selectively granting the UPDATE privilege.
8.4.4 Allowing grantees to grant privileges.

8.5 Taking Back Privileges: The REVOKE Statement.

9 TRANSACTION PROCESSING

9.1 A Transaction as a Fundamental Unit of Work: The COMMIT and
ROLLBACK commands.
9.1.1 A Practical Example of Transaction Processing.

9.2 Transactions From Multiple Users: Concurrency Control.
9.2.1 The Lost Update Problem.
9.2.2 The Temporary Update Problem.
9.2.3 The Incorrect Summary Problem.

9.2.4 Data Locking.

10 THE DATABASE SYSTEM CATALOG

10.1 The DBMS Needs to Manage it's Resources: A typical system catalog.
10.1.1 Table information in the system catalog.
10.1.2 View information in the system catalog.
10.1.3 Column information in the system catalog.
10.1.4 User information in the system catalog.
10.1.5 Privileges and other information in the system catalog.
10.1.6 Commenting the tables, views and columns.

11 USING SQL WITH A HOST LANGUAGE.

11.1 SQL is not a Computer Programming Language: Why SQL needs a host
language.

11.2 How Embedded SQL Programs are Processed: Compile, Bind and Link.

11.3 How SQL Statements Are Embedded: The EXEC SQL clause.

11.4 How SQL Talks to the Host Language: Host language variables.

11.5 Handling Queries That Retrieve Multiple Rows: The SQL cursor.
11.5.1 Selects with Cursors.
11.5.2 Deletes and Updates with Cursors.

11.6 SQL Statements That Fail: Error Handling.

11.7 Dealing With NULL Values: Indicator Variables.

11.8 A Library of SQL Functions: The SQL API.

APPENDIX A The ANSI/ISO standard data types.

APPENDIX B The Sample University Administration Database.

Chapter 1 INTRODUCTION.

The Structured Query Language, SQL is a query language which is used with relational
databases. This chapter starts by describing some of the terms used in data processing
and how they relate to SQL. The later part of this chapter describes relational
databases and how SQL is used to query them.

1.1 "A Collection of Related Data":
Databases and Database Management Systems.

Let's start from basics. What is a database? In very general terms, a database is a
collection of related data. Notice the word related, this implies that the collection of
letters on this page do not by themselves constitute a database. But if we think of them
as a collection of letters arranged to form words, then they can be conceptualised as
data in a database. Using similar reasoning, we can also say that a tome such as a
telephone directory is also a database. It is a database first, because it is a collection of
letters that form words and second, because it is an alphabetical listing of people's
names, their addresses and their telephone numbers. How we think of a database
depends on what use we want to make of the information that it contains.

So far, we have talked about a database in it's broadest sense. This very general
definition is not what most people mean when they talk about a database. In this
electronic age, the word database has become synonymous with the term
"computerised database". Collins English Dictionary describes a database as "A store
of a large amount of information, esp. in a form that can be handled by a computer." In
this book, we will be dealing only with computerised databases. In keeping with popular
trend though, we will be using the word database to refer to a computerised database.

A database (computerised database remember) by itself, is not much use. The data is
stored electronically on the computer's disk in a format which we humans cannot read
or understand directly. What we need is some way of accessing this data and
converting it into a form which we do understand. This is the job of the database
management system or DBMS for short. A DBMS is essentially a suite of programs that
act as the interface between the human operator and the data held in the database.
Using the DBMS, it is possible to retrieve useful information, update or delete obsolete
information and add new information to the database. As well as data entry and
retrieval, the DBMS plays an important role in maintaining the overall integrity of the
data in the database. The simplest example of is ensuring that the values entered into
the database conform to the data types that are specified. For example, in the
telephone book database, the DBMS might have to ensure that each phone number
entered conforms to a set format of XXX-XXXXXXX where X represents an integer.

1.2 "The Database as a Collection of Tables":
Relational databases and SQL.

In the early days of computerised databases, all large database systems conformed to
either the network data model or the hierarchical data model. We will not be discussing
the technical details of these models except to say that they are quite complex and not
very flexible. One of the main drawbacks of these databases was that in order to
retrieve information, the user had to have an idea of where in the database the data
was stored. This meant that data processing and information retrieval was a technical
job which was beyond the ability of the average office manager. In those days life was
simple. data processing staff were expected to prepared the annual or monthly or
weekly reports and managers were expected to formulate and implement day to day
business strategy according to the information contained in the reports. Computer
literate executives were rare and DP staff with business sense were even more rare.
This was the state of affairs before the advent of relational databases.

The relational data model was introduced in 1970, E. F. Codd, a research fellow
working for IBM, in his article `A Relational Model of Data for Large Shared Databanks'.
The relational database model represented the database as a collection of tables which
related to one another.

Unlike network and hierarchical databases, the relational database is quite intuitive to
use, with data organised into tables, columns and rows. An example of a relational
database table is shown in Figure 1.1. We can see just by looking at Figure 1.1 what
the table is. The table is a list of people's names and telephone numbers. It is similar to
how we might go about the task of jotting down the phone numbers of some of our
friends, in the back of our diary for example.

NUM SURNAME FIRSTNAME PHONE_NUMBER
--- ------- --------- ------------
1 Jones Frank 9635
2 Bates Norman 8313
3 Clark Brian 2917
4 Stonehouse Mark 3692
5 Warwick Rita 3487

Figure 1.1

The relational data model consists of a number of intuitive concepts for storing any type
of data in a database, along with a number of functions to manipulate the information.

The relational data model as proposed by Codd provided the basic concepts for a new
database management system, the relational database management system (RDBMS).
Soon after the relational model was defined, a number of relational database languages
were developed and used for instructing the RDBMS. Structured Query Language
being one of them.

The SQL language is so inextricably tied to relational database theory that it is
impossible to discuss it without also discussing the relational data model. The next two
sections briefly describe some of the concepts of this model.

1.2.1 Tables, columns and rows.

We have already seen that a relational database stores data in tables. Each column of
the table represent an attribute, SURNAME, FIRSTNAME, PHONE_NUMBER for
example. Each row in the table is a record. In the table in Figure 1.1, each row is a
record of one person. A single table with a column and row structure, does not
represent a relational database. Technically, this is known as a flat file or card index
type database. Relational databases have several tables with interrelating data.
Suppose that the information in the table of Figure 1.1 is actually the list of people
working in the company with their telephone extensions. Now we get an idea that this
simple table is actually a small part of the overall database, the personnel database.
Another table, such as the one in Figure 1.2. could contain additional details on the
persons listed in the first table.

NUM D_O_B DEPT GRADE
--- ----- ---- -----
2 12/10/63 ENG 4
5 07/05/50 DESIGN 7
3 03/11/45 SALES 9
1 09/03/73 ENG 2

Figure 1.2

1.2.2 The Primary key and the foreign Key.

The two tables described in the previous section and shown in Figures 1.1 and 1.2, now
constitute a relational database. Of course, in a real personnel database, you would
need to store a great deal more information and would thus need a lot more related
tables.

Notice that the first column in each table is the NUM column. The information stored in
NUM does not really have anything to do with the person's record. Why is it there? The
reason is that NUM is used to uniquely identify each person's record. We could have
used the person's name, but chances are that in a large company, there would be more
than one person with the same name. NUM is known as the primary key for the table of
Figure 1.1. For the table of Figure 1.2, where a primary key of another table is used to
relate data, NUM is a called a foreign key.

The primary keys and foreign keys are a very important part of relational databases.
They are the fields that relate tables to each other. In the table of Figure 1.2 for
example, we know that the first record is for Norman Bates because the value for NUM
is 2 and we can see from the table of Figure 1.1 that this is Norman Bates' record.

1.3 "Communicating to the DBMS what you want it to do":
Introduction to the SQL language.

The Structured Query Language is a relational database language. By itself, SQL does
not make a DBMS. It is just a medium which is used to as a means of communicating to
the DBMS what you want it to do. SQL commands consist of english like statements
which are used to query, insert, update and delete data. What we mean by `english
like', is that SQL commands resemble english language sentences in their construction
and use. This does not mean that you can type in something like "Pull up the figures for
last quarter's sales" and expect SQL to understand your request. What it does mean is
that SQL is a lot easier to learn and understand than most of the other computer
languages.

SQL is sometimes referred to as a non-procedural database language. What this
means is that when you issue an SQL command to retrieve data from a database, you
do not have to explicitly tell SQL where to look for the data. It is enough just to tell SQL
what data you want to be retrieved. The DBMS will take care of locating the information
in the database. This is very useful because it means that users do not need to have
any knowledge of where the data is and how to get at it. Procedural languages such as
COBOL or Pascal and even older databases based on the network and hierarchical
data models require that users specify what data to retrieve and also how to get at it.
Most large corporate databases are held on several different computers in different
parts of the building or even at different geographic locations. In such situations, the
non-procedural nature of SQL makes flexible, ad hoc querying and data retrieval
possible. Users can construct and execute an SQL query, look at the data retrieved,
and change the query if needed all in a spontaneous manner. To perform similar
queries using a procedural language such as COBOL would mean that you would have
to create, compile and run one computer programs for each query.

Commercial database management systems allow SQL to be used in two distinct ways.
First, SQL commands can be typed at the command line directly. The DBMS interprets
and processes the SQL commands immediately, and any result rows that are retrieved
are displayed. This method of SQL processing is called interactive SQL. The second
method is called programmatic SQL. Here, SQL statements are embedded in a host
language such as COBOL or C. SQL needs a host language because SQL is not really
a complete computer programming language as such. It has no statements or
constructs that allow a program to branch or loop. The host language provides the
necessary looping and branching structures and the interface with the user, while SQL
provides the statements to communicate with the DBMS.

1.4 "A Research Project Conducted by IBM":
The history of SQL.

The origins of the SQL language date back to a research project conducted by IBM at
their research laboratories in San Jose,
California in the early 1970s. The aim of the project was to develop an experimental
RDBMS which would eventually lead to a marketable product. At that time, there was a
lot of interest in the relational model for databases at the academic level, in conferences
and seminars. IBM, which already had a large share of the commercial database
market with hierarchical and network model DBMSs, realised quite quickly that the
relational model would figure prominently in future database products.

The project at IBM's San Jose labs was started in 1974 and was named System R. A
language called Sequel (for Structured English QUEry Language) was chosen as the
relational database language for System R. In the project, Sequel was abbreviated to
SQL. This is the reason why SQL is still generally pronounced as see-quel.

In the first phase of the System R project, researchers concentrated on developing a
basic version of the RDBMS. The main aim at this stage was to verify that the theories
of the relational model could be translated into a working, commercially viable product.
This first phase was successfully completed by the end of 1975, and resulted in a
rudimentary, single-user DBMS based on the relational model.

The subsequent phases of System R concentrated on further developing the DBMS
from the first phase. Additional features were added, multi-user capability was
implemented, and by 1978, a completed RDBMS was ready for user evaluation. The
System R project was finally completed in 1979. During this time, the SQL language
was modified and added to as the needs of the System R DBMS dictated.

The theoretical work of the System R project resulted in the development and release in
1981 of IBM's first commercial relational database management system. The product
was called SQL/DS and ran under the DOS/VSE operating system environment. Two
years later, IBM announced a version of SQL/DS for the VM/CMS operating system. In
1983, IBM released a second SQL based RDBMS called DB2, which ran under the
MVS operating system. DB2 quickly gained widespread popularity and even today,
versions of DB2 form the basis of many database systems found in large corporate
data-centres.

During the development of System R and SQL/DS, other companies were also at work
creating their own relational database management systems. Some of them, Oracle
being a prime example, even implemented SQL as the relational database language for

their DBMSs concurrently with IBM.

Today, the SQL language has gained ANSI (American National Standards Institute) and
ISO (International Standards Organization) certification. A version of SQL is available
for almost any hardware platform from CRAY supercomputers to IBM PC
microcomputers. In recent years, there has been a marked trend for software
manufacturers to move away from proprietary database languages and settle on the
SQL standard. The microcomputer platform especially has seen a proliferation of
previously proprietary packages that have implemented SQL functionality. Even
spreadsheet and word processing packages have added options which allow data to be
sent to and retrieved from SQL based databases via a Local Area or a Wide Area
network connection.

1.5 "SQL Commands Build Upon Themselves":
Organization of this book.

After this introduction, this book first presents the SQL language in a nutshell.
Subsequent chapters then focus on explaining each of the SQL command groups (the
SELECT, the UPDATE, the CREATE etc) more fully. The reason for this method of
presentation is that a lot of the SQL commands build upon themselves. For example,
you cannot discuss the INSERT INTO with SELECT command without having
knowledge of and understanding the SELECT statement itself. So where do you put the
chapter on INSERT INTO with SELECT? You can't put it before the chapter on
SELECT because as we've said, it requires the reader to have knowledge of the
SELECT statement. You can't put it after the chapter on SELECT because the SELECT
statement requires data to be input into the tables by using the INSERT statement. We
have gone for the second option because it is a lot easier to take a leap of faith and
believe that somehow the tables are already populated with data and use SELECT to
query them rather than trying to understand the INSERT INTO with SELECT without
any knowledge of how SELECT works.

To save having to put phrases such as "see the later chapter on SELECT" or "see the
earlier chapter on INSERT" throughout the book, we have started off by describing the
SQL language globally, and then detailing each command group separately. It's a bit
like a course for auto mechanics, say, you start off by first describing the layout of the
car and all it's major parts such as the engine, the gearbox etc., before going on to
discuss topics like the detailed construction of the engine.

Primarily, this book is designed to teach you how to use SQL to create, modify, maintain
and use databases in practical situations. It is not intended to be an academic treatise
on the subject, and so does not go into the mathematical basis of the topics considered.
What it does contain is lots of examples and discussions on how they work. You should
work your way through this book by reading through a section, and actually trying out
each SQL query presented for yourself. If you do not have access to an SQL based
database, then you can order a fully functional ANSI/ISO SQL database at an
affordable price, by sending off the order form at the back of this book. The quickest
and easiest method of learning SQL (or indeed any computer language) is to use it in
real life, practical situations. The chapters of this book are laid out so that each section
builds upon the information and examples presented in the previous chapters. By
following the SQL query examples, you will create a database, populate it and then use
it to retrieve information.

Remember that the SQL queries in this book are only given as examples. They
represent one possible method of retrieving the results that you want. As you gain
confidence in SQL, you may be able to construct a more elegant query to solve a
problem than the one that we have used. This just goes to show the power and
flexibility of SQL.

The structure of this book is such that as you progress through it, you will be exposed to
more and more complex aspects of SQL. If you follow through the book, you will find
that you are not suddenly presented with anything particularly difficult. Rather, you will
be gradually lead through and actively encouraged to try out SQL queries and variations
of queries until you have thoroughly understood the underlying ideas.

The chapters will not all take the same amount of time to read and understand. You will
benefit most if you sit down, start at a new section, and work your way through until it is
completed. Although we understand that you may find some of the longer sections
difficult to finish in one session. You should nonetheless endeavour to complete each
section in as few sittings as possible. Taking short breaks to think over concepts
learned as you progress through the section is also a good idea as it reinforces the
learning process. You should try to understand the underlying concepts of what you are
learning rather than coasting through the book.

1.5.1 Notational conventions.

The following notational conventions are used throughout this book:

BOLD TYPE These are keywords and data in a statement. They are to appear
exactly as they are shown in bold.

{ } Curly braces group together logically distinct sections of a
command. If the braces are followed by an asterix (*), then the
section inside them can occur zero or more times in a statement. If
followed by a plus (+), then the section inside must appear at least
once in the statement.

[ ] Square brackets are used to signify sections of a statement that
are optional.

( ) Parentheses in bold are part of the SQL command, and must
appear as shown. Parentheses which are not in bold are to
indicate the logical order of evaluation.

... The ellipses show that the section immediately proceeding them
may be repeated any number of times.

| The vertical bar means "or".

Throughout this book, SQL command structure will be explained by using examples of
actual statements.

Chapter 2 A ROUGH GUIDE TO SQL

This chapter presents an overview of the SQL language. The major commands are
described from a functional point of view. Emphasis is given on briefly describing the
SQL statements used in creating, populating, querying and modifying the database
tables. It is left to the later chapters to give a detailed description of each command.
This chapter gives you a feel for the SQL language and it's main command groups.

2.1 "Consider the Simple Address Book":
A Basic Relational Database.

As we have already seen, the relational database model represents the database as a
collection of tables which relate to each other. Tables consist of rows and columns. The
column definitions describe the fields in the table, while the rows are the data records in
the table. For example, consider the simple address book. If we wanted to computerise
this, we could represent it as a relational database table. The table would consist of
columns and rows. For a typical address book, the table column headings might be
SURNAME, FIRSTNAME, TELEPHONE, ADDRESS, RATING, as in Figure 2.1, where
RATING is a measure of how close a friend the person is! Notice how the column
headings for a table appear exactly as they would in a written version of the address
book. The sequence in which the columns are defined when the table is first created is
important to SQL. This will be most evident when we come to adding data using the
INSERT command. The column names in a table must all be different but you can use
numbers to distinguish between similar columns. For example NAME1 and NAME2 are
valid column names. In practice though, this would be a poor choice because they do
not describe the contents of the columns in any way. A much better choice would have
been something like FIRSTNAME and INITIALS. The columns are a method of giving
the table a structure in which to add our data records. You can think of a database table
as a blank sheet of paper. The overall objective is to use that sheet to store the names
and addresses of people we know.

SQL Tips

IBM's DB2 restricts user names to 8 characters but allows 18 characters in table
and column names.

The actual entries that you make into the table will form the rows (or records). So
('Jones', 'Andrew', '(0523) 346639' '767 The Firs LE4 6TY' 15554) is a valid record in
the

SURNAME FIRSTNAME TELEPHONE ADDRESS RATING
------- --------- --------- ------- ------

Jones Andrew (0523) 346639 267 The Firs LE4 6TY 15554
Mason James (0553) 786139 1933 Tripsom Close 12224
Malins Dick (0553) 867139 1966 Gt Glenn Rd 13444
McGinn Mick (0525) 567139 145 Glossop St 15664
Walsh Paul (0553) 656739 The Manor LE6 9PH 16778

Figure 2.1

table of Figure 2.1. Note how the data in the record row is organised in the same
sequence as the column headings in the table.

As we have defined it, the address book table is a pretty bad database. In order to
understand what exactly is wrong with our table, we need to consider some "what if"
situations.

- What would happen if two or more people lived at the same address? We would
need to have a separate entry for each friend, but with the same ADDRESS field
contents.

- What if some of the people have more than one phone number? We would need
to have a separate row in our table for each phone number.

These two "what ifs" show that the current address book definition will lead to
disorganised rows and a lot of redundant data (in the more than one contact phone
number example for instance, we would have two rows with exactly the same
information except for the PHONE_NUMBER field).

Fortunately, the relational database model lets us create multiple related tables to form
a database. When analyzing a real life problem (such as the address book problem), a
formal method of resolving the tables' columns and their relationships can be used. This
method, known as Data Normalization, was first suggested by Codd in 1972. Although it
is beyond the scope of this book to discuss Data Normalization fully, the contents of the
next few paragraphs derive from this method.

Logically, we can split up the address book into three tables. The first table to hold
details of who our friends are, the second to hold details of where they live, and the third
table to hold details of phone numbers where they can be contacted. We don't really
need a table for the ratings because a friend cannot have more than one rating at the
same time. So we can add RATING to the NAMES table. If we wanted to keep a
historical record of the ratings, then we would have to have a separate table for ratings
as well.

Figure 2.2 shows how our address book can be split up to form a true relational
database. Each table has a new field, FRNO. This field is the primary key in the
NAMES table, and a foreign key in the other tables. It is what relates the tables to each
other. A record which refers to a particular friend will have the same FRNO in all the
tables. Thus, for our friend who has two houses, there will be an entry in tables one and
three and two entries in table two.

FRNO SURNAME FIRSTNAME RATING
---- ------- --------- ------

1 Jones Andrew 15554
2 Mason James 12224
3 Malins Dick 13444
4 McGinn Mick 15664
5 Walsh Paul 16778
The NAMES Table

FRNO ADDRESS
---- -------

1 267 The Firs LE4 6TY
2 1933 Tripsom Close
3 1966 Gt Glenn Rd
4 145 Glossop St
5 The Manor LE6 9PH

The ADDRESS Table

FRNO TELEPHONE
---- ---------

1 (0523) 346639
2 (0553) 786139
3 (0553) 867139
4 (0525) 567139
5 (0553) 656739

The TELEPHONE_NUMBER Table

Figure 2.2

In this simple example, the splitting up of the database into three tables is not very
practical. For a personal address book, we would have been better off with the flat file
(single table) database The point to note though is that the three table version of the
database is more flexible (we can store the details of a friend even if he has 25
telephones and 14 houses, without having to store redundant data). For large and
complex databases which may consist of dozens of tables and tens of thousands of
records, this logical splitting up of data into separate tables (known as Data
Normalization) is vital in preventing data redundancy and creating a relationally correct
database.

2.2 "SQL Commands Fall into Different Categories":
Subdivisions of SQL.

The SQL language as defined by ANSI is subdivided into a number of different sections.
This means that the SQL commands fall into different categories depending on what
function they perform.

The Data Definition Language or DDL, (called Schema Definition Language by ANSI)
consists of those commands in SQL that directly create database objects such as
tables, indexes views. A lot of SQL commands also create temporary database objects
during processing. The SELECT command for example, creates a temporary table to
hold the results of a query. Such commands are not part of the DDL.

The Data Manipulation Language or DML consists of those commands which operate
on the data in the database. This includes statements which add data to the tables as
well as those statements which are used to query the database.

A third, unofficial, subdivision of SQL commands is Data Control Language or DCL. It is
generally used to refer to those SQL commands are used for data security. These are
commands that are used to determine whether a user is allowed to carry out a
particular operation or not. The ANSI standard groups these commands as being part of
the DDL.

2.3 "Enter SQL Statements at the Command Line":
Using Interpretive SQL.

All SQL statements in this book have been run using the Data-Lab SQL RDBMS and
interpreter. The interpreter is the interface that you use to communicate with the DBMS.
It allows you to type, compose and edit your SQL queries and has special editing
commands to help you with this. When you are satisfied with the wording of the query,
you can enter a semicolon character which instructs the interpreter to pass the query on

to the SQL engine for processing.

If you are using a different SQL interpreter, you will in most cases, not need to modify
the SQL statements because they follow the ANSI standard quite closely. Where
extensions to the ANSI standard are discussed, you will need to consult the reference
manual for your product to find out the exact form of the statement. Note that since
Data-Lab SQL is quite close to Oracle SQL, Oracle users should have no problems.

2.4 "Use the CREATE TABLE statement":
Creating Database Tables.

The SQL command to create tables is the CREATE TABLE statement.
We will use this to create a simple car dealership database which will be used
throughout the rest of this chapter. This simple database consists of the three tables
shown in Figure 2.3. The CARS table holds details of the car's model name, the body
style and the year of manufacturer. The MD_NUM field is used as the primary key. The
SPECS table stores the information on additional equipment on each of the cars. The
STOCK table holds details of the number of cars of each model that are currently in
stock, and their retail price.

CARS SPECS STOCK
---- ----- -----
MD_NUM MD_NUM MD_NUM
MD_NAME MPG QTY
STYLE RADIO PRICE
YEAR ENGINE

Tables used in the used car dealership database

Figure 2.3

To create the first table in the car dealership database:

CREATE TABLE CARS (
MD_NUM INTEGER,
MD_NAME CHAR(10),
STYLE CHAR(6),
YEAR INTEGER );

Table CARS created.

This statement creates a database table on disk, and assign it the name CARS. The
table's columns are also defined along with their data types. When you create tables,
each of the columns must be defined as a specific data type. For example, the
MD_NUM column is defined as an INTEGER, and MD_NAME is defined as CHAR(10).
This means that when data is added to the table, the MD_NUM column will only hold
integers and the MD_NAME column will hold character string values up to a maximum
of 10 characters. The subject of data types and valid and invalid values will be
discussed in detail in the next chapter.

Now that we have seen how to use the CREATE TABLE statement, we can create the
next two tables in our car dealership database by typing:

CREATE TABLE SPECS (
MD_NUM INTEGER,
MPG INTEGER,
RADIO CHAR(3),
ENGINE CHAR(7) );

Table SPECS successfully created.

CREATE TABLE STOCK (
MD_NUM INTEGER,
QTY INTEGER,
PRICE INTEGER );

Table STOCK successfully created.

When created, the tables are empty. In order to be of any use they need data. The next
section describes the INSERT statement which is used to add data to the tables.

SQL Tips

Oracle allows you to use up to 30 characters for both table and column names.

2.5 "Use the INSERT INTO Statement":
Adding Data to Tables.

Data is added to tables by using the INSERT statement. The values that we need to
add to the car dealership database are shown in Figure 2.4

MD_NUM MD_NAME STYLE YEAR
------ ------- ----- ----
1 HONDA COUPE 1983
2 TOYOTA SALOON 1990
3 BUICK ESTATE 1991
4 NISSAN VAN 1992
5 FORD SALOON 1993

The Cars Table

MD_NUM MPG RADIO ENGINE
------ --- ----- ------
1 43 YES 2L-4CYL
2 25 NO 4L-V8
3 18 YES 5L-V8
4 50 NO 2L-4CYL
5 45 YES 3L-V6

The Specs Table

MD_NUM QTY PRICE
------ --- -----
1 10 4980
2 3 13865
3 5 14900
4 1 11000
5 2 24600
The Stock Table

Figure 2.4

Starting with the first table, the CARS table, the first record or row is added by:

INSERT INTO CARS (MD_NUM, MD_NAME, STYLE, YEAR)
VALUES (1, 'HONDA', 'COUPE', 1983);

1 row successfully inserted.

The rest of the rows can be added to CARS by using exactly the same statement
format, but changing data values each time.

VALUES (2, 'TOYOTA', 'SALOON', 1990);

VALUES (3, 'BUICK', 'ESTATE', 1991);

VALUES (4, 'NISSAN', 'VAN', 1992);

VALUES (5, 'FORD', 'SALOON', 1993);

4 rows successfully inserted.

In the form of the INSERT statement that we have used above, you must specify three
pieces of information. First, the name of the table to insert data into. Second, the names
of the columns where data is to be added. Finally, you need to specify the actual data
values.

We can add data to the SPECS table by:

INSERT INTO SPECS VALUES (1, 43, 'YES', '2L-4CYL');

INSERT INTO SPECS VALUES (2, 25, 'NO', '4L-V8');

INSERT INTO SPECS VALUES (3, 18, 'YES', '5L-V8');

INSERT INTO SPECS VALUES (4, 50, 'NO', '2L-4CYL');

INSERT INTO SPECS VALUES (5, 45, 'YES', '3L-V6');


and to the STOCK table by:

INSERT INTO STOCK VALUES (1, 10, 4980);






The INSERT statements for the SPECS and the STOCK table did not use a value list.
This is a shortcut which SQL allows you to use when you specify values for all the
columns in each row, as we have been doing.

2.6 "Use the SELECT Statement":
Extracting data from tables.

The most important job of any database is to provide you with information. In SQL, the
act of retrieving information is called querying the database. Information is retrieved
from the database by using the SELECT statement.

The previous two sections, first created the car dealership database, them added data
to it. To retrieve the data from the CARS table of this database for example, you could
use a SELECT statement. A SELECT statement is also called a query because it
interrogates the database:

SELECT MD_NAME, STYLE, YEAR
FROM CARS;

MD_NAME STYLE YEAR
---------- ------- --------
HONDA COUPE 1983
TOYOTA SALOON 1990
BUICK ESTATE 1991
NISSAN VAN 1992
FORD SALOON 1993

The data retrieval requirements vary from user to user. For example, in our car
dealership database, one user might want to know how many Nissan cars there are in
stock while another might need to know how many cars there are which have a radio,
eight cylinders and cost less than 10,000. As long as the information that you require is
stored in the database in some form, you will be able to construct a form of SELECT
statement which retrieves it. It because of this flexibility that the SELECT statement is
the most complex and also the most useful of all the SQL commands.

2.7 "Use the UPDATE and DELETE Statements":
Modifying data.

In daily use, a database is a constantly changing store of data. The SQL commands
which are used to modify data that is already in the database are the UPDATE and the
DELETE commands. For example, to change the record of the Ford model in the CARS
table to show the year of manufacture as 1989 and not 1993:

UPDATE CARS
SET YEAR = 1989
WHERE MD_NAME = 'FORD';

1 row updated.

We can express what this query is doing in words as "Update the CARS table and set
the YEAR column value to 1989 for all those records where the MD_NAME column
value is FORD." An important point to note is that UPDATE is capable of modifying the
values of more than one record in a table. So if the CARS table had several Fords, then
this statement would have changed the date of manufacture on all of them to 1989. You
need to be wary of this when modifying values with UPDATE. The trick is to be so
specific in the WHERE clause that only those records that you want to be changed are
changed.

Another reason for wanting to modify the database is when deleting unwanted records
from the tables SQL uses the DELETE command for this.

For example, if we decide that the Ford model in the CARS table is not available for
sale, we can simply delete it's record from the table by:

DELETE FROM CARS
WHERE MD_NAME = 'FORD' AND YEAR = 1989;

1 row deleted.

Just as with the UPDATE statement, care must be taken when using DELETE to
ensure that only those records that you want deleted are actually deleted. The WHERE
clause in this statement is a little more specific than the one we used in the last
UPDATE statement. It asks SQL to delete only those records where the MD_NAME is
Ford and also the YEAR is 1989. To confirm that the DELETE statement did remove
the record for the Ford, we can query the CARS table:

SELECT *
FROM CARS;

-------- --------- ------- --------
1 HONDA COUPE 1983
2 TOYOTA SALOON 1990
3 BUICK ESTATE 1991
4 NISSAN VAN 1992

2.8 "Another Kind of Table, Called a Virtual Table":
Views.

The tables that you have been using up to now are called base tables. There is another
kind of table, called a virtual table or view that is allowed for in SQL. Base tables are
database objects whose structure and the data they contain are both stored on disk.
Views are tables whose contents are derived from base tables. Only their structure is
stored on disk.

SQL's DML statements operate on views just as they do on base tables, but with one
exception: when data is apparently added to, deleted or modified from a view, the actual
data that is operated on is that in the underlying base tables that make up the view.

You can think of a view as a stencil or a window into a table or tables. Suppose that in a
company personnel database, a staff table contains relevant work related information
on employees such as department, supervisor, date joined etc. The table might also
contain sensitive information such as salary, home address and telephone number etc.
An excellent method of limiting casual user access to only the relevant work related
information, and restrict access to the sensitive information would be to use a view.

In the used car dealership database for example, if the manager decides that he does
not want everyone to see the price of the cars in the STOCK table, he could create a
view called NO_PRICE:

CREATE VIEW NO_PRICE
AS SELECT MD_NUM, QTY FROM STOCK;

View NO_PRICE successfully created.

Notice that the CREATE VIEW statement contains a SELECT statement as well. A view
is in fact just a stored query that gets executed whenever the view is used as the
subject of a command. The results of the query define the records `held' in the view ie.
the data in the view.

Once created, the view's definition is stored by the DBMS and can be queried just like a
regular base table. For example, to list all the "rows" in NO_PRICE:

SELECT *
FROM NO_PRICE ;

MD_NUM QTY
-------- --------
1 10
2 3
3 5
4 1
5 2

Notice that the view only displays two columns from the STOCK table. PRICE has been
hidden from the user.

2.9 "Prevent Access to Sensitive Information":
Database security.
As we have already seen, views can be used to prevent access to sensitive information
in the database. Another method of enforcing security is by use of the GRANT and the
REVOKE statements.

SQL operates on the concepts of user identification, ownership of database objects,
and that of granting and revoking privileges from users. When a table is first created, it
is owned by the user who created it. This means that the user who created the table is
automatically given full privileges to operate on that table (INSERT data, UPDATE
values, DELETE rows etc). All other users are given no privileges on the table.

Let's see how this works. We will first create a view called NEW_CARS (which consists
of cars whose year of manufacture is after 1990). We'll create this view under the user
ID of JOE. Don't worry too much if the format of the CREATE VIEW appears a little
strange. What it is doing is to temporarily set the user ID to JOE, then create the view,
then revert back to the original user ID:

CREATE SCHEMA AUTHORIZATION JOE
CREATE VIEW NEW_CARS
AS SELECT * FROM CARS WHERE YEAR > 1990 ;

View NEW_CARS successfully created.

Now if we try to look at the data in the view:

SELECT *

FROM NEW_CARS;

Error 98: User does not have the
necessary SELECT privileges.

SQL tells us that we do not have the necessary privileges for this operation. We can
confirm that the owner of the view, JOE, is allowed to look at the information by
prefixing the JOE user-id to the viewname:

SELECT *
FROM JOE.NEW_CARS ;

--------- ----------- ------- --------
3 BUICK ESTATE 1991
4 NISSAN VAN 1992

This query tells SQL that we know the user ID of the person who created the table is
JOE, and we want it to use this for retrieving data from NEW_CARS.

Chapter 3 CREATING AND MAINTAINING TABLES

Before you can do anything in SQL, someone must first create a database structure
composed of related tables, and then add data to those tables. The CREATE TABLE
command is used to create new tables and is a part of SQL's DDL. This chapter starts
by considering the DDL as defined by the ANSI/ISO standard. The later sections of this
chapter describe how to create, alter and delete SQL tables. All the commands
described in this section are concerned with operations on the tables themselves and
do not directly affect the data stored in them.

Indexes are a method of speeding up the querying of tables, and these are also
introduced in this chapter.

3.1 "The ANSI Standard Makes Such a Distinction...":
The DDL and the ANSI/ISO standard.

The ANSI/ISO standard defines the SQL language as being composed of the Data
Manipulation Language (DML) and the Data Definition Language (DDL). The DDL can
be broadly considered as those SQL commands that operate on the structure of the
database, ie. the database objects such as tables, indexes and views. The DML on the
other hand, can be thought of as those statements that operate on the data stored in
the database tables themselves.

The ANSI/ISO standard makes such a distinction between these two aspects of SQL,
that it considers them as two separate sub-languages. Indeed, once the database
structure has been created, the ANSI/ISO standard does not even require the RDBMS
to accept any DDL statements. This means that the ANSI/ISO standard divides the
database development and creation activities from the database utilization activities.
This is not the case in commercial SQL based RDBMSs where almost all allow the
database development activities and the database utilization activities to be carried out
jointly, with no separation between the DDL statements and the DML statements. This
allows a minimal database to be created, populated with data and used while at the
same time, the structure of the database is broadened.

It is obvious that the ANSI/ISO method of separating the development activity from the
utilization activity will lead to complications when it comes to altering the structure of the
database, for instance, when it comes to removing a table. In fact, the ANSI/ISO
standard does not even define the DROP TABLE statement to delete a table from the
database or the ALTER TABLE statement to change the structure of a table. One of the
few advantages of the ANSI/ISO method is that it forces you to adopt a rigorous
systems analysis strategy before committing the final database design. Subsequent
changes to the database structure will mean system down time so you have to think
hard to design the right system before you start using it.

3.2 "Single and Multiple Database Architectures":
The structure of SQL databases.

The ANSI/ISO SQL standard specifies that the database schema consist of a single
large database with tables which are owned by various users. The ownership of the
tables sub-classifies them into different virtual database groups. This is shown in Figure
3.1. The tables owned by FRANK_G might be the Accounts (sub)database and those
owned by MARK_B, the Suppliers (sub)database. Under the ANSI/ISO standard, both
the Accounts tables and the Suppliers tables are part of the overall system database.
All the tables in such single-database architectures can easily reference each other.
The single-database architecture is used in both the Oracle and IBM's DB2 systems.

-------------------------------------------------------------
| |
| ____________________ ________ ___________________ |
| |TABLE 1 TABLE 2 | |TABLE 3 | |TABLE 4 TABLE 5 | |
| | | | | | | |
| |TABLE 7 TABLE 9 | |TABLE 67| |TABLE 56 TABLE 45| |
| | | | | | | |
| |TABLE 56 TABLE 19| |TABLE 82| |TABLE 92 TABLE 23| |
| |____________________| |________| |___________________| |
| Owned by FRANK_G Owned by Owned by MARK_B |
| SALLY_R |
| |
| |
| |
| |
| |
| |
-------------------------------------------------------------
System Database

ANSI/ISO structure consists of one large system database, which
all tables are a part of. Tables are owned by users.

Figure 3.1

One of the disadvantages of the single-database architecture is that over time, as more
and more tables are added to the system, the database becomes very big and bulky.
Performing database administration tasks such as back-ups, performance analyzing
etc. on such large databases becomes a complex process, requiring the services of a
dedicated database administrator. A database architecture which does not suffer from
this disadvantage is the multiple-database architecture. Here, tables are organized into

several distinct databases. This is shown in Figure 3.2. Although the data is split into
several smaller, more manageable databases, the multiple-database architecture does
suffer from a serious disadvantage. The tables in one database cannot (easily) contain
foreign key references to keys in another database's table. The multiple-database
architecture is used in Sybase, SQL Server and Ingres systems.

The ACCOUNTS database
____________________
|TABLE 1 TABLE 2 |
| |
|TABLE 7 TABLE 9 |
| |
|TABLE 56 TABLE 19|
|____________________|
Owned by FRANK_G

The CUSTOMERS database
________
|TABLE 3 |
| |
|TABLE 67|
| |
|TABLE 82|
|________|
Owned by SALLY_R

The SUPPLIERS database
___________________
|TABLE 4 TABLE 5 |
| |
|TABLE 56 TABLE 45|
| |
|TABLE 92 TABLE 23|
|___________________|
Owned by MARK_B

Figure 3.2

3.3 "Creating a database table":
The CREATE TABLE command.

Creating database tables is done through the CREATE TABLE command. The
CREATE TABLE command is one of three SQL statements that are part of the DDL
and are used to manipulate the structure of tables that constitute a database. The other
two are ALTER TABLE and DROP TABLE. We'll meet these later in this chapter.

The syntax of the CREATE TABLE statement is shown in Figure 3.3. The CREATE
TABLE command creates an empty table-one with no records. The parameters that you
must supply are name of the table, a list of the columns in the table and a description of
the columns (data type, size etc). A valid table must have at least one column but there
is usually no upper limit specified.

CREATE TABLE table [ READ_ONLY ]
( element { , element } ) ;

table
The table name. This can be up to 24 characters.

element
column definition | unique constraint definition

column definition
col_name col_type [ NOT NULL ] [ UNIQUE | INDEX ]

col_name
The name of the column can be up to 24 characters.
col_type
ANSI/ISO columns can be of the following type:
CHAR [(length)] |
VCHAR [(length)] |
NUMERIC [(precision [,scale])] |
DECIMAL [(precision [,scale])] |
INTEGER |
SMALLINT |
FLOAT [(precision)] |
DOUBLE PRECISION |
DATE
unique constraint definition
UNIQUE ( col_name {, col_name }* )

Figure 3.3

ANSI/ISO SQL also allows you to create READ ONLY tables. This means that once
created, SQL commands cannot be used to insert or update or delete data any in the
tables. Creating READ ONLY tables only makes sense if some non-SQL process (an
application program for example) is going to add the data.

The data types allowed in the column definitions vary considerably from product to
product. Most commercial SQL systems support the ANSI data types as a minimum,
and add additional types that are proprietary. The valid ANSI/ISO data types are given
in Appendix A.

SQL Tips

IBM's DB2 lets you store oriental language characters such as Kanji in fixed and
variable length strings of 16-bit characters.

The UNIQUE and the INDEX column modifiers both create indexes for the field to which
they are applied. Indexes will be discussed in detail in the next section. The NOT NULL
column modifier adds the condition that a record cannot be inserted into the table if no
value is supplied for this particular field.

In SQL, tables are owned by the user who created them. Initially, only the table's owner
is allowed to perform any operations involving that table. Other users must refer to the
table by preceding the table name with the owner's user's ID. A table which is meant for
use by all users can be created under a special user identifier known as PUBLIC.
Tables created under PUBLIC allow all users on the system to access them. We came
across the concept of ownership and privileges briefly in chapter 2. The subject of table
ownership is discussed more fully in the chapter on database security.

The names of tables which are owned by any given user must all be different. Some
systems extend this so that the names of all the tables in the whole system must be
different from each other. This also applies to column names within a table, but
separate tables can however, have repeating column names.

We will be using a database based on a university administration system throughout
this book. The database consists of five tables: STUDENTS, LECTURERS,
SUBJECTS, EXAMS and DEPARTMENTS. The whole database will be created and
used in stages as we progress through the chapters. The structure of the tables as well
as the data in them is shown in Figure 3.4. Appendix B gives an in depth description of
this sample database.

SURNAME FIRST_NAME D_O_B STUDENT_NO DEPT_NO YEAR
------- ---------- ----- ---------- ------- ----
Duke Fitzroy 11-26-1970 1 4 2
Al-Essawy Zaid M A 11-26-1970 2 4 2
Ayton Phil J M A 07-13-1967 3 3 1
Patel Mahesh 12-07-1970 4 2 1
Jones Gareth P Y 01-24-1970 5 2 1
Scott Gavin T J 02-20-1971 6 2 2
Baker Abu-Mia 03-13-1971 7 4 1
Brown Joseph P A 04-19-1970 8 3 3
Monkhouse Robert Jones 05-23-1967 9 1 1
Grimm Hans Johan 06-21-1971 10 2 1
Gyver Sue L J V 07-30-1968 11 4 2
Hung-Sun Jimmy Lau 08-11-1969 12 1 3
Middleton Jane P 09-14-1971 13 1 3
Mulla Farook F U 10-24-1968 14 3 2
Layton Hugh 11-16-1971 15 5 1
Wickes Wendy Y Y W 12-05-1969 16 1 1

THE STUDENTS TABLE

SURNAME INITL LECT_NO DEPT_NO SUB_N GRADE PAY JOINED
------- ----- ------- ------- ----- ----- --- ------
Jones R A 1 1 2 E 24000 03-25-1990
Scrivens T R 2 3 1 D 31800 09-30-1986
Nizamuddin W M 3 3 4 A 86790 05-26-1969
Campbell J G 4 5 3 C 43570 02-23-1980
Ramanujan S 5 4 5 C 40900 01-01-1985
Finley G Y 6 4 5 D 34210 03-28-1960

THE LECTURERS TABLE

SUB_NO SUB_NAME DEPT_NO CREDITS PASS
------ -------- ------- ------- ----
1 Mathematics 1 2 65
2 English Lit 2 1 60
3 Engineering Drwg 1 1 71
4 Basic Accounts 3 1 67
5 Industrial Law 4 2 52
6 Organic Chemistry 5 3 57
7 Physiology 6 3 78
8 Anatomy 6 1 74
9 Electronics 1 3 71
10 Marketing 3 2 56

THE SUBJECTS TABLE

Figure 3.4

SUB_NO STUDENT_NO MARK DATE_TAKEN
------ ---------- ---- ----------
1 1 76 05-23-1984
9 1 42 05-20-1984
3 1 67 05-15-1984
2 2 52 06-05-1984
2 3 89 06-08-1984
2 3 51 05-11-1984
4 4 34 05-11-1984
10 4 49 06-26-1984
5 5 62 05-03-1984
5 6 70 05-17-1984
5 7 36 05-23-1984
5 8 52 05-20-1984
6 9 67 05-15-1984
6 10 82 06-05-1984
6 11 73 06-08-1984
7 12 27 05-11-1984
8 12 56 05-11-1984
8 13 67 06-26-1984
7 13 63 05-03-1984

THE EXAMS TABLE

DEPT_NO DEPT_NAME HEAD BUDGET P_BUDGET
------- --------- ---- ------ --------
1 Engineering 59 5780000 6200000
2 Arts & Humanities 23 753000 643000
3 Management Studies 3 2510000 1220000
5 Physical Sciences 18 4680000 4250000
6 Medicine 67 6895000 6932000

THE DEPARTMENTS TABLE

Figure 3.4 .....continued

Let's begin by creating the first table in our university admin. system, the STUDENTS
table:

CREATE SCHEMA AUTHORIZATION PUBLIC
CREATE TABLE STUDENTS (
SURNAME CHAR(15) NOT NULL,
FIRST_NAME CHAR(15),
D_O_B DATE,
STUDENT_NO INTEGER NOT NULL UNIQUE,
DEPT_NO INTEGER,
YEAR DECIMAL(2) );

Table STUDENTS successfully created.

This query instructs the system to create a new table called STUDENTS with six fields.
When the table is first created, it contains no data rows. We have used CREATE
TABLE in conjunction with the CREATE SCHEMA statement because the ANSI/ISO
standard specifies that the CREATE SCHEMA statement forms the basis of the
CREATE TABLE statement as well as the CREATE VIEW and the GRANT statements.
The CREATE SCHEMA statement tells SQL who the owner of the newly created table
is to be and this can be different from the current user-id.

Strictly speaking, ANSI SQL does not allow the CREATE TABLE statement to be used
without the CREATE SCHEMA clause but almost all popular versions of SQL allow
CREATE TABLE statements to be used without the preceding CREATE SCHEMA
clause. In this case, the current authorization identifier will be assigned as the owner of
the table.

The order in which the columns are defined is important. The column order is the
default order in which the results are displayed whenever you query the table.

In the STUDENTS table, SURNAME, FIRST_NAME, D_O_B, ... are all column names.
Columns in the same table must each have unique column names but columns in
different tables can have the same name.

The CREATE TABLE statement must define the data type for each column,
immediately after the column name. Data types define the type of the data that can be
stored in the column. For example, if a column is defined as being DECIMAL data type,
than it will only hold decimal values. Trying to store text strings in the column will cause
an error. Appendix A describes the ANSI/ISO standard data types, but these are not the
only types that are available in commercial SQL systems. Almost all the popular SQL
RDBMSs support the ANSI/ISO data types as a minimum set but add to it substantially.
Some of the more common additional data types include money, date and boolean (to

store true/false values).

SQL Tips

DB2 supports three different date and time data types: DATE, TIME and
TIMESTAMP which is used to specify an instant in time.

Now let's construct the other tables that form the sample database. The LECTURERS
table holds details of the teaching staff at the university. To create the LECTURERS
table:

CREATE TABLE LECTURERS (
INITL CHAR(4),
LECT_NO INTEGER NOT NULL,
DEPT_NO INTEGER,
SUB_NO INTEGER,
GRADE CHAR(1),
PAY DECIMAL(6),
JOINED DATE,
UNIQUE (SURNAME, LECT_NO) );

Table LECTURERS successfully created.

Notice that this time, the unique constraint definition is at the end of the CREATE
TABLE statement. ANSI/ISO SQL allows you to specify a group of columns as being
UNIQUE in this way. This differs from using the UNIQUE keyword as a column modifier
in that SQL considers the combination of fields (SURNAME and LECT_NO in this case)
to be unique.

SQL Tips

OS/2 Extended Edition does not support date arithmetic.

The SUBJECTS table holds the details of the subjects taught at the university. To
create the SUBJECTS table:

CREATE TABLE SUBJECTS (
SUB_NO INTEGER NOT NULL UNIQUE,
SUB_NAME CHAR(20),
DEPT_NO INTEGER,
CREDITS NUMERIC(2),
PASS NUMERIC(2) );

Table SUBJECTS successfully created.

The EXAMS table holds the details of the exams taken by the students and the results
they achieved. To create the exams table:

CREATE TABLE EXAMS (
SUB_NO INTEGER NOT NULL,
STUDENT_NO INTEGER NOT NULL,
MARK DECIMAL(3),
DATE_TAKEN DATE );

Table EXAMS successfully created.

The DEPARTMENTS table holds the details of all the university departments. To create
the departments table:

CREATE TABLE DEPARTMENTS (
DEPT_NO INTEGER NOT NULL,
DEPT_NAME CHAR(20),
HEAD INTEGER,
BUDGET DECIMAL(10),
P_BUDGET DECIMAL(10),
UNIQUE (DEPT_NO) );

Table DEPARTMENTS successfully created.

SQL Tips

Oracle's DATE type stores both date and time down to a second accuracy. In this
respect, it is similar to DB2's TIMESTAMP type.

3.4 "Apply Restrictions to Groups of Columns":
Column and table modifiers.

The CREATE TABLE command allows you to specify column modifiers, such as NOT
NULL for the DEPT_NO column in DEPARTMENTS and UNIQUE for the
STUDENT_NO column in STUDENTS. These modifiers tell SQL to control the data that
can be input into the column. The CREATE TABLE command also lets you specify
table modifiers which apply restrictions to groups of columns such as the UNIQUE table
modifier in the LECTURERS table definition which applies the UNIQUE constraint to
both the SURNAME and the LECT_NO fields jointly.

3.4.1 The NOT NULL modifier.

The NOT NULL modifier prevents NULL (a token that designates a column as being
empty) values from appearing in the column. This means that a row cannot be added to
the table if values for the NOT NULL columns is not supplied. NOT NULL is usually
used for primary keys, for which there must be a value for all rows in the table.

3.4.2 The UNIQUE modifier.

The UNIQUE modifier is used in the STUDENTS table on the SURNAME and the
STUDENT_NO fields. UNIQUE ensures that the values entered into the column are all
different from each other. Rows cannot be added to the table if the value for a UNIQUE
column is already in the table. It only makes sense to apply UNIQUE to columns that
are also declared NOT NULL. If this is not done, then only one row will be allowed to
have a NULL value for that column because the UNIQUE constraint will prevent other
NULLs from being entered. Subsequent rows will thus have the NOT NULL constraint
applied by default.

SQL Tips

Most commercial SQL systems use the non-standard CREATE INDEX statement
to specify a column as being unique.

3.4.3 The INDEX modifier.

The INDEX modifier is not part of the ANSI/ISO standard but is quite common in
commercial SQL systems. INDEX causes an index to be created based on the values
in the column, which greatly speeds up query processing. Almost all commercial SQL
systems also create an index for columns that are defined as UNIQUE. Index
maintenance is taken care of by the DBMS, and so the user is not always aware of
when indexes are being created.

3.4.4 The PRIMARY KEY modifier.

The PRIMARY KEY modifier is a relatively new feature in SQL which is not available in
all systems. It modifier enables us to tell SQL which columns in our tables are the
primary keys. Up to now, we have been dealing with primary keys as logical concepts
only. This modifier enables us to extend this so that we can formally define primary
keys. For example, in the SUBJECTS table, we said that the SUB_NO column is the
primary key. To formally define this, the CREATE TABLE statement would be:

SUB_NAME CHAR(20),
DEPT_NO INTEGER,
CREDITS NUMERIC(2),
PASS NUMERIC(2),
PRIMARY KEY (SUB_NO) );


Note that a column must be declared as NOT NULL before the PRIMARY KEY modifier
can be applied to it.

3.4.5 The FOREIGN KEY modifier.

This modifier is closely related to the PRIMARY KEY modifier. Most tables contain
references to primary keys in other tables, called foreign keys. SQL allows you to
define these relations when you create the table. In the SUBJECTS table for
example, the DEPT_NO column is a foreign key. Thus a more complete CREATE
TABLE statement would be:


SUB_NAME CHAR(20),
DEPT_NO INTEGER,
CREDITS NUMERIC(2),
PASS NUMERIC(2),
PRIMARY KEY (SUB_NO),
FOREIGN KEY (DEPT_NO)
REFERENCES DEPARTMENTS );


This statement tells SQL that the DEPT_NO column is a foreign key in this table
what references the DEPARTMENTS table. Since there can only be one primary key
for each table, the DBMS knows that the DEPT_NO column (foreign key in
SUBJECTS) references the DEPT_NO column (primary key) in the DEPARTMENTS
table.

3.4.6 The DEFAULT modifier.

The ANSI/ISO standard allows you to define default values that columns should have.
Usually, if no value is supplied for a column, then it is assigned the NULL value. The
DEFAULT column modifier overrides this. In the SUBJECTS table for example, if the
default pass mark for an exam is 65%, then we can set this at the CREATE TABLE
stage by:

SUB_NAME CHAR(20),
DEPT_NO INTEGER,
CREDITS NUMERIC(2),
PASS NUMERIC(2) DEFAULT 65,


You should use default values where you would otherwise have to type repetitive
data. Such as the city column in an address table, where most of the values might be
for the same city. Default values can also be used as an alternative to NULLs. NULL
values appear false in any comparison operations and hence tend to be excluded in
a lot of SQL queries where their inclusion might give more meaningful results.

3.4.7 The CHECK modifier.

Some of the columns in the tables you create will have a range of acceptable values or
the values may need to be entered in a particular format. The CHECK modifier allows
you to tell SQL about these acceptable values or format. In the EXAMS table for
example, the CREDITS awarded for a subject must be greater than 0 and the maximum
number of credits that can be awarded for any subject is 10. This can be expressed in
the CREATE TABLE statement as:

SUB_NAME CHAR(20),
DEPT_NO INTEGER,
CREDITS NUMERIC(2) CHECK
(CREDITS > 0 AND CREDITS <= 10),
PASS NUMERIC(2) DEFAULT 65,


As with the other column modifiers, CHECK can also be applied as a table constraint.
This is useful where CHECK is to be applied to more than one column. Thus for the
LECTURERS table, if a salary of 100,000 or more is only allowed if the lecturer is on
seniority grade A or B, then we could use CHECK as a table modifier:

CREATE TABLE LECTURERS (
INITL CHAR(4),
DEPT_NO INTEGER,
SUB_NO INTEGER,
GRADE CHAR(1),
PAY DECIMAL(6),
JOINED DATE
UNIQUE (SURNAME, LECT_NO),
CHECK (PAY < 100000 OR GRADE <= 'B') );


3.5 "Indexes are Ordered for Extremely Fast Searches":
Inxexes.

An index is a database object created and maintained by the DBMS. It is essentially a
list of the contents of a column or group of columns. Indexes are ordered so that
extremely fast searches can be conducted through them to find data. The rows in tables
are not ordered in any particular sequence, they are merely stored in the order in which
they were inserted into the table. As most large SQL databases have tables with
thousands or even millions of rows, searching through them to find particular values can
become quite time consuming. Indexes speed up this search process by keeping a
sorted list of values which the DBMS can search through.

How does the DBMS use indexes? To answer this question, let's consider an example.
Figure 3.5 shows a table in a corporate database. Assume that it holds records of all
potential suppliers listed for all the cities of the world. If we run the query for an un-
indexed table, SQL would need to look through 99003 rows before it found the record
for Hyderabad, which we wanted. In executing such a query, SQL starts at record 1 and
checks if the condition CITY = 'HYBD' is true. If it is, then the record is retrieved into the
results table. SQL then moves on to record 2 and repeats the process. This is done until
it reaches the last row in the table.

QUERY: SELECT * FROM SUPPLIERS
WHERE CITY = 'HYBD';

. . . C_NUM QTY CITY . . . . . .
----- --- -----
~ ~ ~
~ ~ ~
~ ~ ~
99001 934 N.Y.
99002 467 LOND
99003 12 HYBD <----- The query needs to look through
99004 456 L.A. | 99005 rows to find these records.
99005 23 HYBD <-----

The SUPPLIERS table

Figure 3.5

Now let's see how the query is speeded up by using an index. If the CITY field was
indexed, then the index will keep an ordered list of all the data in the CITY column as
well as information that tells the DBMS where to find each record on disk. Figure 3.6
shows how the index is organized. To resolve the query with condition CITY = 'HYBD',
the DBMS only needs to scan through the index and find the first entry for HYBD. The
index tells the DBMS where to look in the table to find the actual record. The index
holds all the entries for HYBD sequentially, and so the database can quickly refer to the
index to find all the rows where CITY = 'HYBD'.

KEY-FIELD LOCATION
.
.
.
HAMBURG 0033433344
HELSINKI 0042124442
HYBD 0001276900
HYBD 0001276902
JHBG 0034412344
KBUL 0056789877
.
.
.

The KEY-FIELD is an ordered list of the CITIES column in the
SUPPLIERS table.

The LOCATION is number that tells the DBMS exactly where on the
disk to find each record.

Figure 3.6

Although indexing tables has many advantages, it also has disadvantages. Indexes use
up additional disk space, and also when tables are added to, deleted from or the values
of indexed columns are modified, the DBMS needs to maintain the index as well. This
additional makes INSERT, UPDATE and DELETE commands run slower.

Indexes are created with the non ANSI/ISO CREATE INDEX command. On most
systems, this command also lets you specify the name of the index to be created.
Although you will not be allowed to directly manipulate the index in any way, the index
name is useful when you want to delete the index.

To create an index on the CITY column of the SUPPLIERS table in Figure 3.5 for
example:

CREATE INDEX SUPP_CTY_IDX
ON SUPPLIERS (CITY) ;

Index SUPP_CTY_IDX successfully created.

We could have also used CREATE UNIQUE INDEX instead of CREATE INDEX. The
UNIQUE keyword tells SQL that the CITY columns can only contain unique values.
Recall that the ANSI/ISO standard allows you to specify UNIQUE as a column modifier
in the CREATE TABLE statement itself.

As CREATE INDEX is not a part of the ANSI/ISO standard, systems vendors allow
many additional clauses to this command that deal with the physical characteristics of
the index to be created.

Indexes created with the CREATE INDEX command can later be deleted by the DROP
INDEX command. To get rid of the SUPP_CTY_IDX index:

DROP INDEX SUPP_CTY_IDX ;

Index SUPP_CTY_IDX successfully dropped.

3.6 "Changing the Structure of a Table":
The ALTER TABLE Command.

The ALTER TABLE command allows a user to change the structure of a table. New
columns can be added with the ADD clause. Existing columns can be modified with the
MODIFY clause. Columns can be removed from a table by using the DROP clause.
The syntax of the ALTER TABLE command is shown in Figure 3.7.

ALTER TABLE tbl_name
ADD (
column definition [ BEFORE col_name ]
{ , column definition [BEFORE col_name ] }* )

DROP ( col_name { , col_name }* )

MODIFY ( column definition { , column definition }* ) ;

tbl_name
The name of the table to alter.

col_name
The name of the column to alter.

column definition
See the CREATE TABLE section for the syntax of column
definition.

Figure 3.7

The ALTER TABLE command is not part of the ANSI/ISO standard. According to
ANSI/ISO reasoning, you should have designed your tables on paper first, and
subsequent alterations to them should not be necessary. The nonstandard nature of the
ALTER TABLE command means that, all the commercial dialects of SQL implement
different clauses and command syntax.

On most systems, you are allowed to add more than one column with a single ALTER
TABLE command. However, you should not count on this feature. To add the
departmental phone number column to the DEPARTMENTS table for example:

ALTER TABLE DEPARTMENTS
ADD (PHONE_NO CHAR(12) BEFORE HEAD);

Table DEPARTMENTS successfully altered.

This query alters the structure of the DEPARTMENTS table by adding an additional
column called PHONE_NO The BEFORE clause is optional and tells SQL to position
the new column immediately before the column called HEAD. The new structure of the
table is shown in Figure 3.8. If the BEFORE clause is omitted, then the new column will
be added at the end of the existing columns ie. after P_BUDGET Most dialects of SQL
set the values in the newly added column to NULL for all extant rows, but as ever, this
should not always be assumed.

DEPT_NO DEPT_NAME PHONE_NO HEAD BUDGET P_BUDGET
------- ----------- --------- ---- ------- --------
1 Engineering ? 59 5790000 6200000
2 Art & Humanities ? 23 753000 643000
3 Management Studies ? 34 2510000 1220000
4 Industrial Law ? 12 78000 210000
5 Physical Sciences ? 18 4680000 4250000
6 Medicine ? 67 6895000 6932000

The ? in the PHONE_NO column indicates a NULL value added to the
column values for all extant rows.

Figure 3.8

Most forms of ALTER TABLE also allow you to delete columns from tables. Thus:

ALTER TABLE DEPARTMENTS DROP (PHONE_NO);

Table DEPARTMENTS successfully altered.

will remove the PHONE_NO column from the DEPARTMENTS table.
Once the column is dropped, then the data is lost. It cannot be retrieved.

The MODIFY clause of the ALTER TABLE command allows you to modify the UNIQUE
or the NOT NULL status of a column. To make more extensive changes to a column,
you should DROP it and then ADD it with the changes incorporated.

You should only modify the UNIQUE or NOT NULL status of a column if the table is
empty. If the UNIQUE or the NOT NULL status of a column is modified on a non-empty
table, an error may occur because duplicate or NULL values of that column may
already exist in the table data. Changing the structure of a table already populated with
data is risky to say the least. On corporate databases especially, even on the best
administered system, there are always some views created by users or embedded SQL
programs which may no longer function because they relied on the previous structure of
the modified table. Modifications need to be carefully planned and implemented.

For a well designed table, you should never need to change the constraints (UNIQUE,
NOT NULL etc.) on a table column and you should only use the ALTER TABLE
command as a last resort, when all else fails. An alternative to using the ALTER TABLE
command is to simply create a new table with the modified structure and populate it with

data from the old table. (A simple way of doing this is to use the INSERT command with
a SELECT * query. This is discussed in full in the next chapter).

Remember that in order to be able to use the ALTER TABLE command, in the first
place, you must be either the table's owner or have been granted ALL PRIVILEGES for
the table by the owner.

3.7 "Remove Redundant Tables from the Database":
The DROP TABLE Command.

As your database evolves, you will eventually want to remove redundant tables from the
database. The DROP TABLE command is used to delete a table from the database.
Some DBMSs require that the table to be eliminated must be empty before it can be
dropped from the database. This is used as a safety feature, to prevent accidental
deletion of tables that are still in use. You should not count on this and should always
delete tables with extreme care.

Since the DROP TABLE option removes all trace of the table as far as SQL is
concerned, it is important to ensure that no command files, embedded SQL programs
or columns from other tables refer to the dropped table's fields in the form of foreign
keys. Also, the table should not be accessed by any VIEWS but most implementations
of SQL are smart enough to prevent you from deleting tables that have associated
views.

To delete the STUDENTS table for example:

DROP TABLE STUDENTS ;

Table STUDENTS successfully dropped.

Although DROP TABLE is not part of the ANSI standard (ANSI specifies no means of
destroying table definitions) it is nonetheless, a very useful command for restructuring
and maintaining your database.

Chapter 4 QUERYING SQL TABLES

A query is a method of interrogating an SQL database. It is used to tell the DBMS what
information you want it to retrieve from the database and also how you want the data to
appear. When you think about it, the only reason for storing and maintaining a database
of information is to make it easy to get at the information that you need, when you need
it. One of the most important functions of any query language is to make the retrieval of
information as easy and also as powerful as possible for the user. Data retrieval needs
to be easy because most of the time, the people who query the database are not the
same people who programmed the database. Users are not interested in the
technicalities of how the database is organized or how it is managed. The query
language needs to have easy to understand (preferably plain english) commands that
users can use intuitively. The query language also needs to be powerful because it
needs to be capable of providing users with all the information that they may want. As
the DBMS has no idea of what the user queries are going to be beforehand, the
language constructs must be powerful enough to deal with all the requests the user is
likely to make.

4.1 "The most basic query":
The Simple SELECT statement.

The SELECT statement allows you to specify the data that you want to retrieve, what
order to arrange the data, what calculations to perform on the retrieved data and many,
many more operations. As it's the only SQL verb that enables you to query the
database and SQL is a query language, it is necessarily the most complex of all SQL
commands. ANSI/ISO SQL allows up to six different clauses in the SELECT statement
of which the first two are mandatory. The syntax of the full SELECT statement is shown
in Figure 4.1.

The simple SELECT statement, as the name implies, is the most elementary form of
query which uses only the mandatory clauses of the full SELECT. It only requires you to
supply two pieces of information. First, the columns that you wish to see, and second,
the name of the table that the columns are in. For example, this query retrieves all the
rows in the DEPARTMENTS table:

SELECT DEPT_NO, DEPT_NAME, HEAD, BUDGET, P_BUDGET
FROM DEPARTMENTS ;

------- --------------------- --------- ------------ -----------
1 Engineering 59 5780000 6200000
2 Arts & Humanities 23 753000 643000

6 Medicine 67 6895000 6932000
SELECT [ DISTINCT ] field_expression { , field expression }*
FROM table_spec { , table_spec }*
[ WHERE search condition ]
[ ORDER BY field_name {, field_name }* ]
[ GROUP BY field_name {, field_name }* ]
[ HAVING condition ]
;

field_expression
The field expression may be one of the following:
- Field name eg SNO, S.SNO.
- ANSI aggregate function SUM(), AVG(), MIN(), MAX() and
COUNT().
- * is a special field expression which means select all
fields.

table_spec
The name for the table(s) to select from.

search condition
The WHERE search condition specifies what records are to be
retrieved in the SELECT.

field_name
The field name may be up to 24 characters in length.

condition
The HAVING condition is used to eliminate some groups from a
SELECT query.

Figure 4.1

You can specify more than one table name in the FROM clause,
but in this case, SQL will produce a listing of all the rows
from the second named table for each row in the first named
table. This is known as the cartesian product of the tables.
For example:

SELECT DEPT_NAME, SUB_NAME
FROM DEPARTMENTS, SUBJECTS ;

DEPT_NAME SUB_NAME
--------------------- ---------------------
Engineering Mathematics

Engineering English Lit
Engineering Engineering Drwg
Engineering Basic Accounts
Engineering Industrial Law
Engineering Organic Chemistry
Engineering Physiology
Engineering Anatomy
Engineering Electronics
Engineering Marketing
Arts & Humanities Mathematics
Arts & Humanities English Lit
Arts & Humanities Engineering Drwg
Arts & Humanities Basic Accounts
Arts & Humanities Industrial Law
Arts & Humanities Organic Chemistry
Arts & Humanities Physiology
Arts & Humanities Anatomy

The information retrieved by a cartesian product query can quickly grow if more than
two tables are specified. For three tables of 100 rows each, a cartesian product
SELECT will produce 1 million result rows. In most cases, the results are not of much
use as they do not easily relate to real life situations.

To find out what the simple SELECT does, let's have a closer look at what the query
we've just used is telling the DBMS; "SELECT the DEPT_NO, the DEPT_NAME, the
HEAD, the BUDGET and the P_BUDGET columns FROM the DEPARTMENTS table".
When you read it out like this, it is obvious what information this query is requesting
from the DBMS. In most versions of interpreted SQL, the results are displayed as soon
as the DBMS finishes executing the query. In most cases, the results appear on the
screen as they are shown in this book. Column names are at the top with the columns
shown in the order in which they were specified in the SELECT statement. If more
columns are specified in the SELECT statement than can fit on the screen, on some
systems they are split up on two or more lines. Other systems allow you to scroll up,
down, left or right through the results by using the arrow keys. The second method is
better because when results columns are split up on different lines, the formatting is lost
and data appears to be displayed haphazardly.

The query result rows are not listed in any particular order. The DBMS just lists the rows
in the order in which it comes across them in the table.

Note that all SQL queries (and other statements too, for that matter) end with the
semicolon character. Newline can be used to format the query into clauses so that it is
easier to understand what the query is doing when you refer to it several weeks later
say. Most SQL interpreters and programs treat the newline and the tab characters as
equivalent to the space character. You can type all SQL statements on a long single
line if you wanted. To tell SQL that you have finished entering the query, you must type
the semicolon character at the end.

To retrieve all the columns from a table, SQL allows you to use the asterisk, *, character
as a shortcut. Thus the following query is exactly the same as the previous query where
we retrieved all the columns from the DEPARTMENTS table:

SELECT *
FROM DEPARTMENTS ;

------- ------------------- --------- ------------ ------------
1 Engineering 59 5780000 6200000
2 Arts & Humanities 23 753000 643000
6 Medicine 67 6895000 6932000

In place of the asterisk, you should read "all the fields" in the SELECT statement. Notice
how the columns in the results appear in the order in which they were defined when the
table was created.

So far, we have looked at SELECT statements that retrieve all the columns from a
table. In most cases, we are only interested in certain columns in a table. SQL allows us
to specify these columns in the first clause of the SELECT. As an example, say we
wanted to look at the pass mark for each subject in the SUBJECTS table, we are only
interested in the SUB_NAME and the PASS columns in the SUBJECTS table:

SELECT PASS, SUB_NAME
FROM SUBJECTS ;

PASS SUB_NAME
----- --------------------
65 Mathematics
60 English Lit
71 Engineering Drwg
67 Basic Accounts
52 Industrial Law
57 Organic Chemistry
78 Physiology
74 Anatomy
71 Electronics
56 Marketing

If a column list is used the columns in the results table appear in the order in which they

are specified in the SELECT. You can use this fact to change the order in which the
columns appear in the results.

Leaving columns out of the SELECT statement only affects the results of the query. It
does not affect the data in the named table in any way.

As well as simple column names, the SELECT clause also lets you use scalar
expressions and string constants. Scalar expressions are simple calculations performed
on numeric type column values. The results of the calculation are displayed in the
results table as columns. For example, we can use a scalar expression using the
annual pay field to display the monthly pay for each lecturer:

SELECT SURNAME, PAY, (PAY / 12)
FROM LECTURERS ;

SURNAME PAY
---------------- -------- -----------
Jones 24000 2000
Scrivens 31800 2650
Nizamuddin 86790 7232
Campbell 43570 3630
Ramanujan 40900 3408
Finley 34210 2850

The third column in the results table has been generated as a direct result of the PAY /
12 calculation that we specified. The data in this column is not actually stored in any
table, but has been calculated by SQL. In most versions of SQL, expressions are only
allowed to use the addition, subtraction, multiplication and division functions. The fields
used in expressions must be numeric type. Notice that the heading of the generated
column is the expression that we used in the SELECT clause. This feature depends on
the particular version of SQL that you use. Some dialects of SQL have blank headings
for calculated columns.

SQL lets you use string constants in the column list to output text messages. When you
use string constants, the string value will appear in the column position for each row of
the results table. As with all string values, constants must be inside single quotes:

SELECT SUB_NAME, 'has pass mark of', PASS, '%'
FROM SUBJECTS ;

SUB_NAME PASS
--------------------- ----------------- ---- -

Mathematics has pass mark of 65 %
English Lit has pass mark of 60 %
Engineering Drwg has pass mark of 71 %
Basic Accounts has pass mark of 67 %
Industrial Law has pass mark of 52 %
Organic Chemistry has pass mark of 57 %
Physiology has pass mark of 78 %
Anatomy has pass mark of 74 %
Electronics has pass mark of 71 %
Marketing has pass mark of 56 %

SQL Tips

SQL Server, Informix and dBase IV accept string constants enclosed in double
quotes ("...").

In this query, the use of string constants is not very elegant. The same comment
appears for all the result rows. Constants are most useful when used with aggregate
functions that produce a single calculated value based on the data in tables for
example:

SELECT 'The average pass mark is', AVG(PASS), '% per subject'
FROM SUBJECTS ;

AVG(PASS)
------------------------ --------- - -----------
The average pass 65.1 % per subject

AVG(PASS) is an aggregate function which calculates the average value of the PASS
column. This will be discussed further in a later section.

ANSI/ISO SQL defines SELECT statements as part of the DML. ANSI/ISO SQL further
defines DML commands as having the ability to change the data in the database.
SELECT by itself, cannot alter data in the database and so it is not strictly a part of the
DML. Database data is modified only when SELECT is used in conjunction with other
DML commands such as INSERT and UPDATE. It is best to think of the SELECT as
being in a category by itself.

SELECT lets you use the DISTINCT keyword to eliminate duplicate rows from the query
results. Consider the DEPT_NO column in the STUDENTS table. This gives the
department number that each student belongs to. If we simply wanted to know which

departments are represented in the STUDENTS table, we could use the DISTINCT
argument to remove repeat values for this column from the results table:

SELECT DISTINCT DEPT_NO
FROM STUDENTS ;

DEPT_NO
---------
1
2
3
4
5

DISTINCT is very useful in queries where you simply want to know if a value is present
in a table and are not interested in how many times it occurs. DISTINCT itself can only
be used once in a SELECT statement. However, you can specify more than one
column after DISTINCT. In this case, SQL will eliminate those rows where the values
are the same in all the columns.

The opposite of DISTINCT is ALL. This is the default that SQL assumes if neither is
specified. In practice, ALL is not used. It is understood that if DISTINCT is absent, then
the default, ALL is in effect and all columns, including duplicates will be displayed in the
results table.

4.1.1 Calculated columns.
As well as using simple column names, you can also specify scalar mathematical
expressions. These are known as calculated columns; for example:

SELECT DEPT_NAME, (BUDGET + 15000), (BUDGET - P_BUDGET),
(BUDGET * 2.25), (BUDGET / 4.5)
FROM DEPARTMENTS;

DEPT_NAME
-------------------- ------- -------- --------- ----------
Engineering 5795000 -420000 65025000 1284444.4
Arts & Humanities 768000 110000 8471250 167333.3
Management Studies 2525000 1290000 28237500 557777.7
Industrial Law 93000 -132000 877500 17333.3
Physical Sciences 4695000 430000 52650000 1040000
Medicine 6910000 -37000 77568750 1532222.2

This query demonstrates the use of calculated columns. You are allowed to use the
addition, subtraction, multiplication and division mathematical functions with both
numeric constants and column names as long as the columns involved are numeric
type columns. Trying to use non-numeric column types will cause an error.

4.2 "Selecting rows for output":
The WHERE clause.
One of the most useful feature of the SQL query is that it allows you to selectively
retrieve only those rows that interest you. In a large database, with thousands of rows in
each table, you may only be interested in a handful of records at any time. The WHERE
clause of the SELECT statement lets you specify a predicate, which tells SQL what
records are to appear in the results. A predicate is a logical expression that can be
either true or false. As an example, consider in the DEPARTMENTS table, the
predicate "department name is Engineering". For any row in the DEPARTMENTS table,
this predicate is either true or false. The department name is either "Engineering" or it is
not. Now let's use this predicate in the WHERE clause of a SELECT statement:

SELECT *
FROM DEPARTMENTS
WHERE DEPT_NAME = 'Engineering' ;

--------- ------------- ------ --------- ----------
1 Engineering 59 5780000 6200000

Notice that the word Engineering is in single quotes. These must be used to specify all
text strings. This query retrieves all the rows in the DEPARTMENTS table where the
DEPT_NAME is Engineering. In this case, it retrieves only one record. In this query, we
have used the asterisk to retrieve all the columns from DEPARTMENTS in the results.
You do not have to include the columns that appear in the WHERE clause in the
results, but it helps to highlight what the query is doing.

When processing a query with a predicate, the DBMS goes through all the rows in the
table and checks to see if the predicate is true or false for each row. This is the type of
query which is greatly speeded up if the row that is used in the predicate is indexed.

4.2.1 Comparison Test Operators: =, <, <=, >, >=, <>.

In the previous section we saw how predicates evaluate equivalence statements as

either true or false. As well as the equals to operator, (=), SQL also allows you to use
the other comparison operators shown in Figure 4.2. The predicate resolves to either
true or false for each row in the table for all these comparison operators as well. For
example, lets run a query that gets the names of all those lecturers who earn more than
60,000:

Comparison
Operator Relation Example of use
---------- -------- --------------
= Equals to surname = 'Jones'
< Less than mark < 65
> Greater than salary > 45000
<= Equal to or less than surname <= 'Smith'
>= Equal to or greater than date >= 12-Aug-1993
<> Not equal to dept_no <> 14

Figure 4.2

SELECT INITL, SURNAME
FROM LECTURERS
WHERE PAY > 60000;

INITL SURNAME
------ -------------
W M Nizamuddin

The operators shown in Figure 4.2 are standard mathematical signs that act on
numerical information. In SQL predicates, they can also be applied to character type
values. The result of the predicate will depend on the character representation system
used by the computer's operating system. Most microcomputer and minicomputer
systems use the ASCII system. Some large mainframes use a system known as
EBCDIC. Both these systems represent alphanumeric characters as numeric values
that the computer can understand. SQL uses these underlying numeric values as the
basis of comparison. In this book, we will assume that all the examples we use are run
on an ASCII system. As this is the most popular system, this is quite a good
assumption. Let's look at an example. In the STUDENTS table, to list the names of all
the students whose surname begins with characters from M to Z:

SELECT SURNAME, FIRST_NAME
FROM STUDENTS
WHERE SURNAME > 'M';

SURNAME FIRST_NAME
---------------- ---------------
Patel Mahesh
Scott Gavin T J
Monkhouse Robert Jones
Middleton Jane P
Mulla Farook F U
Wickes Wendy Y Y W

SQL Tips

The ANSI/ISO standard specifies the inequality operator as <>. IBM's DB2 and
SQL/DS use ¬= and SQL Server uses !=.

Notice that the rows are not arranged in alphabetical order. SQL lists the rows in the
order in which it finds them in the table. Ordering is possible in SELECT, and this will be
discussed in later sections of this chapter.

In the previous query, we used the uppercase character, M, in the predicate. It is
important to remember that M is not the same as m. If we had used the lowercase
character instead, SQL would not have found any matching records:

SELECT SURNAME, FIRST_NAME
FROM STUDENTS
WHERE SURNAME > 'm';

No matching records found.

The reason for this query coming up empty is that in the ASCII scheme, uppercase
characters are defined as being less (they have a lower underlying numeric value) than
lowercase characters. All the surnames in the STUDENTS table start with an
uppercase letter and so in the ASCII scheme, they are all less than the lowercase m.
The values assigned in ASCII are reversed in EBCDIC, so lowercase characters are
less than uppercase. You need to be sure which scheme your computer system uses
before constructing your queries.

4.2.2 Range Test Operator: BETWEEN.

The BETWEEN range test operator allows you to define a predicate in the from of a

range. If a column value for a row falls within this range, then the predicate is true and
the row will be added to the results table. The BETWEEN range test consists of two
keywords, BETWEEN and AND. It must be supplied with the upper and the lower range
values. The first value must be the lower bound and the second value, the upper bound.
For example, in the LECTURERS table, if we wanted to look at the records of all those
lecturers who earn between 31,800 and 40,900:

SELECT SURNAME, PAY
FROM LECTURERS
WHERE PAY BETWEEN 31800 AND 40900 ;

SURNAME PAY
---------------- ---------
Scrivens 31800
Ramanujan 40900
Finley 34210

This query retrieves three records. Notice that the upper and lower parameters are
inclusive. This means that the rows where pay equals 31,800 (lower bound) and 40,900
(upper bound) are also retrieved in the results. SQL will not allow you to specify the
upper bound first. Thus the following query does not return any records:

SELECT SURNAME, PAY
FROM LECTURERS
WHERE PAY BETWEEN 40900 AND 31800 ;


You can use character values as upper and lower range bounds:

SELECT *
FROM LECTURERS
WHERE SURNAME BETWEEN 'N' AND 'R' ;

SURNAME INITL LECT_NO DEPT_NO SUB_NO GRADE PAY JOINED
---------- ----- ------- ------- ------ ----- ------ ----------
Nizamuddin W M 3 3 4 A 86790 05-26-1969

The query only retrieves one row because Nizamuddin is between N and R, but
Ramanujan is not. When comparing strings of unequal length, SQL pads out the
smaller string with spaces before doing the comparison. As the space character has a
lower value than letter characters in the ASCII scheme, the word Ramanujan falls

outside the upper bound.

BETWEEN does not actually add any new functionality to SQL. All queries that use
BETWEEN can be rephrased to run using only the comparison test operators instead.
For example the last query can be expressed without using BETWEEN as:

SELECT *
FROM LECTURERS
WHERE (SURNAME >= 'N') AND (SURNAME <= 'R') ;

----------- ----- ------- ------- ------ ----- ------ ----------
Nizamuddin W M 3 3 4 A 86790 05-26-1969

The AND keyword is a boolean operator that tells SQL that both expressions inside the
parentheses must be true for the predicate to be true. Although this query is functionally
the same as the previous query, the one using BETWEEN is more elegant and it is
clearer to the reader what the query is trying to achieve.

4.2.3 Set Membership Test Operator: IN.

We've seen that BETWEEN defines a range of values to check against for inclusion or
exclusion from the results table. This is not always enough. What if you needed to
check for certain values only? Values that do not always fit into a neat range. To
accommodate this, SQL allows the use of the IN operator. An example will illustrate the
use of IN. In the SUBJECTS table, if we wanted to look at the rows of the Anatomy and
the Physiology subjects, we could use a query with IN:

SELECT *
FROM SUBJECTS
WHERE SUB_NAME IN ('Anatomy', 'Physiology') ;

------ ------------- ------- ------- ----
7 Physiology 6 3 78
8 Anatomy 6 1 74

You must define the set values within parentheses, and must separate each value with
a comma. In this example, we have used string values. IN also allows other valid data
types to be used as set members for example to list the subjects rows given that their
pass marks are 52, 56 and 57:

SELECT SUB_NAME, PASS
FROM SUBJECTS
WHERE PASS IN (52, 56, 57) ;

SUB_NAME PASS
-------------------- ----
Industrial Law 52
Organic Chemistry 57
Marketing 56

As with all the SQL query commands, the result records are not displayed in any order
unless the ordering is explicitly specified. In the above query for example, we specified
pass marks of 52, 56 and 57 in the inclusion set. The results table displayed the rows in
the 52, 57, 56 order. The reason for this is that this is the order in which the DBMS
found the rows in the table.

As with BETWEEN, IN does not add to SQL's functionality. What IN does can also be
accomplished by using comparison and boolean operators. For example, the previous
query can also be expressed as:

SELECT SUB_NAME, PASS
FROM SUBJECTS
WHERE PASS = 52
OR PASS = 56
OR PASS = 57 ;

SUB_NAME PASS
--------------------- ----
Industrial Law 52
Organic Chemistry 57
Marketing 56

4.2.4 Pattern Matching Test Operator: LIKE.

The LIKE operator is used to match string pattern values. LIKE uses wildcard
characters to specify one or more string character values. ANSI/ISO SQL defines two
wildcard characters, the underscore (_) and the percent (%). These are the characters
that are almost universally used in commercial SQL systems for pattern matching.
String pattern matching is useful in cases where you are not sure of the exact string
value that you need to search for. For example if you cannot remember the spelling of a
person's name:

SELECT *
FROM STUDENTS
WHERE SURNAME LIKE 'A_ton';

--------- ----------- ---------- ---------- ------- ----
Ayton Phil J M A 07-13-1967 3 3 1

The underscore character is one of the wildcards, and is used to represent any valid
character (one only). In this query, we are not sure if the student's surname is spelt as
Ayton or Aeton or even Aiton. The LIKE 'A_ton' predicate tells SQL that the first letter of
the surname is 'A' and the last three letters are 'ton', but we are not sure of the second
letter. If you are familiar with the MS-DOS or OS/2 or UNIX operating systems, then the
_ character performs the same function in SQL as ? does in MS-DOS, and . does in
UNIX.

The previous query told SQL to retrieve those rows where the second letter of the
surname is any valid character. The rest of the pattern ie. the first and the last three
letters must match exactly as specified.

The second wildcard character you can use in LIKE is the percent (%) character. This is
used to represent a sequence of zero or more characters. The percent wildcard in SQL
corresponds to the * wildcard in MS-DOS and OS/2 and UNIX. Let's use percent to look
at the records of all those students whose surname ends in 'ton':

SELECT *
FROM STUDENTS
WHERE SURNAME LIKE '%ton' ;

--------- ----------- ---------- ---------- ------- ----
Ayton Phil J M A 07-13-1967 3 3 1

You can also mix and match the % and the _ wildcard characters in a single query:

SELECT *
FROM STUDENTS
WHERE SURNAME LIKE 'A_t%' ;

--------- ----------- ---------- ---------- ------- ----
Ayton Phil J M A 07-13-1967 3 3 1

The % and _ characters are themselves legal ASCII characters. Using valid characters
as wildcards can cause problems. What if you wanted to use % or _ as part of the string
and not as wildcards? SQL's solution to this is to allow you to define and use the
escape character. The escape character has a special meaning in the LIKE string in
that the character immediately following it is treated as a regular character and not a
wildcard. For example suppose we wanted to search for the string '_search%' where %
and _ are regular characters and not wildcards, then we could use the following query
with the ESCAPE clause:

SELECT *
FROM SUBJECTS
WHERE SUB_NAME LIKE '$_search$%' ESCAPE '$' ;


The ESCAPE clause at the end of the query defines the dollar ($) character as the
escape character. In the string, '$_search$%', % and _ are treated as characters and
not as wildcards. Of course, this query comes up empty because we do not have a
subject called '%search_' in the SUBJECTS table.

SQL Tips

IBM's DB2, OS/2 Extended Edition, Oracle and SQL Server do
not support the ESCAPE clause.

4.2.5 NULL Value Test Operator: IS NULL.

As we know NULL values are used to indicate that no data has been defined yet. This is
different from blank string values or zero numeric values. Blank and zero values are just
that, values. NULL marks the column as not having any definite value. When you use
NULLs in SQL expressions, the result will always be undefined. For example, if you
wanted to look at the rows in the LECTURERS table where the value for the DEPT_NO
field is NULL, the following query will not retrieve the results you want:

SELECT SURNAME, DEPT_NO
FROM LECTURERS
WHERE DEPT_NO = NULL ;

SURNAME DEPT_NO
--------------- -------
Jones 1
Scrivens 3
Nizamuddin 3

Campbell 5
Ramanujan 4
Finley 4

The DBMS retrieved all the lecturers row in our system because the predicate
"DEPT_NO = NULL" is unknown for all the rows. It is neither true nor false. Another
DBMS could just as easily have not retrieved any rows depending upon how it treats
unknown predicate results. SQL provides the IS NULL operator to search specifically for
NULL values. The valid form of the previous query is thus:

FROM LECTURERS
WHERE DEPT_NO IS NULL ;


The NOT logical operator (discussed in the next section) can be used to reverse the
meaning of IS NULL. To retrieve the rows of those lecturers where the DEPT_NO value
is not NULL:

FROM LECTURERS
WHERE DEPT_NO IS NOT NULL ;

SURNAME DEPT_NO
--------------- -------
Jones 1
Scrivens 3
Nizamuddin 3
Campbell 5
Ramanujan 4
Finley 4

NOT can also be used with the other operators, eg NOT BETWEEN and NOT LIKE to
reverse their meaning.

4.2.6 Logical Operators: AND, OR and NOT.

The scope of the WHERE clause and the operators used with it can be extended by
using the logical operators AND, OR and NOT. They enable you to specify compound
search conditions to fine tune your data retrieval requirements. The functioning of these
operators is shown in Figure 4.3. The logical operators link multiple predicates within a
single WHERE clause. For example, to see the records of those subjects which have a

credit value of 1 and whose pass mark value is greater than 70%, we need two
predicates in the WHERE clause:

Logical
Operator Usage Result
-------- --------------------------- --------------------
AND Predicate1 AND Predicate2 Returns true if both
Predicate 1 and
Predicate 2 are true.

OR Predicate1 OR Predicate2 Returns true if either
Predicate 1 or
Predicate 2 are true.

NOT NOT Boolean Expression1 Returns true if
Expression 1 is false.
Returns false if
Expression 1 is true.

Figure 4.3

SELECT SUB_NAME
FROM SUBJECTS
WHERE CREDITS = 1
AND PASS > 70 ;

SUB_NAME
--------------------
Engineering Drwg
Anatomy

The WHERE evaluates to true if both the first predicate (CREDITS = 1) AND the
second predicate (PASS > 70) are true. As with the single predicate query, the DBMS
processes all the rows in the STUDENTS table one by one and checks to see if this
multiple predicate evaluates to true or false for each row. You can use as many logical
operators as you like to link predicates into complex expressions:

SELECT * FROM LECTURERS
WHERE DEPT_NO = 4
AND (GRADE > 'C' OR PAY <= 30000)
AND NOT LECT_NO = 5;

------- ----- ------- ------- ------ ----- ------ ----------
Finley G Y 6 4 5 D 34210 03-28-1960

SQL lets you group expressions by using parentheses. These have the same effect in
SQL expressions as they do in mathematical expressions. The expressions inside the
parenthesis are evaluated first, and are treated as a single expression. In the above
query, AND applies to the expression inside the parenthesis as a whole, ie. GRADE >
'C' OR PAY <= 30000. When you are analyzing complex WHERE clauses, it is best to
break the WHERE into it's constituent predicates and reading them in plain english.
Let's apply this to the last query. The first search condition is "department number is
equal to 4". The AND links this to a parenthesized expression, "either the grade is lower
than C or pay is 30000 or less". You need to be careful here because grade D is lower
than grade C but the character D is greater than C. The last predicate is slightly more
tricky. In english, we would say " lecturer number is not equal to 5". SQL doesn't let you
construct this as LECT_NO NOT = 5. The NOT must precede the boolean expression
that it operates on. If we now put these all together, the WHERE clause can be
expressed as "Where department number is equal to 4 and either the grade is lower
than C or pay is 30000 or less and also, the lecturer number is not equal to 5".

SQL Tips

The ANSI/ISO standard specifies that NOT has the highest precedence, followed
by AND and then OR.

4.3 "Ordering the output of a query":
The ORDER BY clause.

In all the queries we've seen so far, the rows in the results table have not been ordered
in any way. SQL just retrieved the rows in the order in which it found them in the table.
The ORDER BY clause allows you to impose an order on the query results.

You can use ORDER BY with one or more column names to specify the ordering of the
query results. For example, to list student's records in alphabetical order by surname:

SELECT *
FROM STUDENTS
ORDER BY SURNAME ;

---------- ------------- ----------- ---------- ------- ----
Ayton Phil J M A 07-13-1967 3 3 1
Baker Abu-Mia 03-13-1971 7 4 1
Brown Joseph P A 04-19-1970 8 3 3
Duke Fitzroy 11-26-1970 1 4 2

Gyver Sue L J V 07-30-1968 11 4 2
Jones Gareth P Y 01-24-1970 5 2 1
Layton Hugh 11-16-1971 15 5 1
Mulla Farook F U 10-24-1968 14 3 2
Patel Mahesh 12-07-1970 4 2 1
Scott Gavin T J 02-20-1971 6 2 2

The ORDER BY clause only affects the manner in which these rows are displayed by
SQL. If there are NULL values in the ORDER BY column then they appear either at the
beginning or at the end of the list depending on your dialect of SQL.

This query listed the student's rows alphabetically by SURNAME, in ascending order.
This is the default. We can explicitly specify the ordering by using the ASC (for
ascending) and the DESC (for descending) keywords. If we had used DESC in the
previous query:

SELECT *
FROM STUDENTS
ORDER BY SURNAME DESC ;

----------- ------------- ---------- ----------- ------- ----
Wickes Wendy Y Y W 12-05-1969 16 1 1
Scott Gavin T J 02-20-1971 6 2 2
Patel Mahesh 12-07-1970 4 2 1
Mulla Farook F U 10-24-1968 14 3 2
Layton Hugh 11-16-1971 15 5 1
Jones Gareth P Y 01-24-1970 5 2 1
Gyver Sue L J V 07-30-1968 11 4 2
Duke Fitzroy 11-26-1970 1 4 2
Brown Joseph P A 04-19-1970 8 3 3
Baker Abu-Mia 03-13-1971 7 4 1
Ayton Phil J M A 07-13-1967 3 3 1

The students are now listed in reverse alphabetical order. Note that ASC is optional. If
neither DESC OR ASC is specified then ASC is assumed to be in effect.

You can use ORDER BY with more than one column. In this case, SQL will use the first
column as the primary ordering field, the second column as the secondary and so on. In
our STUDENTS table for example, to list the student's records by departments and

within each department by surname:

SELECT *
FROM STUDENTS
ORDER BY DEPT_NO, SURNAME ;

----------- ------------ ---------- ---------- ------- ----
Wickes Wendy Y Y W 12-05-1969 16 1 1
Jones Gareth P Y 01-24-1970 5 2 1
Patel Mahesh 12-07-1970 4 2 1
Scott Gavin T J 02-20-1971 6 2 2
Ayton Phil J M A 07-13-1967 3 3 1
Brown Joseph P A 04-19-1970 8 3 3
Mulla Farook F U 10-24-1968 14 3 2
Baker Abu-Mia 03-13-1971 7 4 1
Duke Fitzroy 11-26-1970 1 4 2
Gyver Sue L J V 07-30-1968 11 4 2

Notice that the rows in the results table are now ordered by the DEPT_NO field. This is
the primary ordering field. Within each department, the students are displayed in
alphabetical order by SURNAME. This is the secondary ordering field. Although you can
use as many ordering fields as you like in the ORDER BY clause, the ANSI/ISO
standard requires that the columns used in the ORDER BY clause are also displayed in
the results table. This means that they must be specified in the SELECT clause, either
explicitly by name, or implicitly by using the asterisk. This ANSI/ISO requirement is not
enforced by all SQL dialects but it is a good idea to adhere to it anyway for portability
reasons.

You have seen how to order results rows by using column names in the ORDER BY
clause. What if you don't know what the column name is? Such situations are not as
remote as you might think. For example calculated columns and aggregate functions
cannot be referred to by their column name. To overcome this, ORDER BY also
accepts column number values. For example, we can list the contents of the
DEPARTMENTS table in allocated budget order either by specifying ORDER BY
BUDGET or by specifying the column number:

SELECT DEPT_NO, DEPT_NAME, BUDGET
FROM DEPARTMENTS
ORDER BY 3 ;

DEPT_NO DEPT_NAME BUDGET

--------- --------------------- ------------
6 Medicine 6895000
1 Engineering 5780000
5 Physical Sciences 4680000
3 Management Studies 2510000
2 Arts & Humanities 753000
4 Industrial Law 78000

The first column specified in the SELECT clause is always column 1. Subsequent
columns have numeric values according to where they are specified in SELECT and not
where they occur in the table itself. This applies to calculated columns as well:

SELECT DEPT_NAME, (BUDGET * 2.25)
FROM DEPARTMENTS
ORDER BY 2 ;

DEPT_NAME
-------------------- -----------
Medicine 775687500
Physical Sciences 526500000
Industrial Law 877500
Management Studies 28237500
Arts & Humanities 8471250
Engineering 65025000

4.4 "Summary of data in tables":
The ANSI aggregate functions.

The rows in a table are elemental pieces of information that you can use to base your
decisions on. Very often, the data that you need can be found directly in one or more
columns. But sometimes, the data is based on the values of all the rows in the table.
For example, if you need to know the average mark in the exams table, you must add
up the marks for all the students, then divide that value by the number of students in the
table. ANSI/ISO SQL provides five functions, known as aggregate functions which can
be used to summarize data in tables. These functions operate on the table data and
produce a single value as output.

The five ANSI/ISO functions are:

COUNT() outputs the number of rows or column values that would be selected by
the query. The function does not actually list any of the rows, but only a value
denoting the total number of rows or column values that the query retrieves.

SUM() outputs the sum total of all the column values that are addressed by the
query. This function can only be used with numeric type columns.

AVG() outputs the average (arithmetic mean) of the column values addressed by
the query. As with the SUM() function, AVG() can only be used with
numeric type columns.

MIN() outputs the minimum, the smallest, column value from those that are
addressed by the query.

MAX() outputs the maximum, the largest, column value from those that are
addressed by the query.

Aggregate functions can be used in the select list just like regular columns with the
following provisions: You cannot nest aggregate functions and you cannot mix regular
columns and aggregate functions in the same query.

4.4.1 The number of values or rows: The COUNT() function.

There are two different versions of the COUNT() aggregate function that ANSI/ISO
allows. The first counts and lists the number of non-NULL values in a particular column.
The second counts and displays the total number of rows that would be retrieved by a
query. These two versions of COUNT() differ only in the arguments that are passed to
them.

Let's use COUNT() to count the number of data values in a column. To find out how
many students have been assigned to a department in the STUDENTS table:

SELECT COUNT(DEPT_NO)
FROM STUDENTS;

COUNT(DEPT_NO)
--------------
16

In our case, all the students are assigned a department number and the number output
by the query is the same as the number of students there are in the table. If this were
not the case, ie. if there were NULL values in the DEPT_NO field for some of the
student's rows, then these rows would not appear in the COUNT() function's total.

To count the number of different values in a column, the column name must be
preceded by the DISTINCT keyword. For example, to look at the number of different
departments that are represented in the DEPT_NO field of the STUDENTS table:

SELECT COUNT(DISTINCT DEPT_NO)
FROM STUDENTS;

COUNT(DEPT_NO)
--------------
5

The output from this query is 5 because there are five different department number
values in this column. The ANSI/ISO standard states that DISTINCT must be used with
column names in the COUNT() function, most commercial versions of SQL relax this
requirement and leave it up to the user to use DISTINCT or not.

As pointed out earlier, the COUNT() function can also be used to count rows in a table
as well as column values. To do this, COUNT() must be used with an asterisk. To count
the number of rows in the EXAMS table:

SELECT COUNT(*)
FROM EXAMS;

COUNT(*)
--------
19

The COUNT(*) total includes all the rows addressed by the query, including NULL and
duplicate rows. If we are only interested in knowing the number of exams taken by a
particular student, we would have to use the WHERE clause to retrieve those rows that
we are interested in:

SELECT COUNT(*)
FROM EXAMS
WHERE STUDENT_NO = 1 ;

COUNT(*)
---------
3

4.4.2 The total of values: The SUM() function.

The SUM() aggregate function calculates the sum total of the values in a column. The
parameter passed to SUM() must be the name of the column either by itself or used in a
scalar expression. The data in the columns used by SUM() must be numeric of type

such as integer, decimal etc. Let's use SUM() to find the total expenditure on staff pay:

SELECT SUM(PAY)
FROM LECTURERS ;

SUM(PAY)
--------
261270

This query adds up all the values in the PAY column and lists the final total. The output
of SUM() (and also the other aggregate functions that deal with numeric type data) is
usually of the same data type as the column data but sometimes, the result is of greater
precision then the column data.

You can use scalar expressions as parameters to the aggregate functions. The
following query adds 1500 to each lecturer's pay and calculates the sum total:

SELECT SUM(PAY), SUM(PAY + 1500)
FROM LECTURERS ;

SUM(PAY) SUM(PAY+1500)
---------- -------------
261270 270270

In this simple example, we could have calculated this value by adding 1500 x 6 = 9000
to the SUM(PAY) value. Scalar expressions are most useful when you want to look at
say, the total expenditure on pay for a percentage increase in salary for each lecturer.
For example, this query finds the total expenditure on pay if we increase each lecturer's
salary by 7.5%:

SELECT SUM(PAY), SUM(PAY * 1.075)
FROM LECTURERS ;

SUM(PAY) SUM(PAY*1.075)
----------- --------------
261270 280865

4.4.3 The average value: The AVG() function.

The AVG() function calculates the average or arithmetic mean of the values in a
column. AVG() can only be applied to numeric type columns and outputs a numeric
value. SQL calculates the average by adding up all the values in the column, then

dividing the total by the number of values. As an example, the following query
calculates the average pay for a lecturer:

SELECT AVG(PAY)
FROM LECTURERS ;

AVG(PAY)
---------
43545

We can also selectively calculate averages. This query finds the average mark obtained
by students in a particular subject:

SELECT AVG(MARK)
FROM EXAMS
WHERE SUB_NO = 5 ;

AVG(MARK)
---------
55

4.4.4 The minimum and maximum values: The MIN() and MAX() functions.

The MIN() function finds the smallest value in a column of data. MIN() can operate on
string and numeric data types as well as non-ANSI types such as date and time. For
example, to find the earliest date when a lecturer joined the staff:

SELECT MIN(JOINED)
FROM LECTURERS ;

MIN(JOINED)
-------------
03-28-1960

SQL Tips

In the EBCDIC character set, which is used in IBM mainframes, the lowercase
characters precede the uppercase characters which precede digits.

Most dialects of SQL treat earlier dates and times as being less than later dates and
times. So to find the last date when a lecturer joined the staff:

SELECT MAX(JOINED)
FROM LECTURERS ;

MAX(JOINED)
------------
03-25-1990

MIN() and MAX() both allow you to use scalar expressions as well as column names as
parameters. For example, if the average pass mark for all subjects was found to be
58%, then this query finds the lowest difference in percentage points between this mark
and the exam marks:

SELECT MIN(58 - MARK)
FROM EXAMS ;

MIN(58 - MARK)
--------------
-31

The query comes up with the answer of -31 because the highest mark in EXAMS is 89
and 58 - 89 = -31. This result may not be what you expected and serves to illustrate an
important point. You need to be careful when wording your queries to ensure that they
do what you intend them to do.

The order of precedence within the data types is shown in Figure 4.4. Remember that
this applies only to the ASCII character scheme.

DATES NUMERIC STRING
----- ------- ------
^ 01-JAN-1980 -100.50 123ABC
| 31-JAN-1980 0.40 ABCDEF
| 01-DEC-1980 0 Abcdef
| 01-JAN-1981 250.30 abcdef
DECREASE

Figure 4.4

SQL Tips

The ANSI/ISO standard specifies that NULL values are ignored by the column
functions.

4.4.5 Sub-totals of values: The GROUP BY clause.

The aggregate functions described in the previous section have been used to produce
grand totals. Values output by them are just like the totals that appear at the end of
each column listing in a report. You can also use these functions to output sub-total
values. The GROUP BY clause of the SELECT statement lets you split up the values in
a column into subsets. The aggregate functions are then applied to these subsets
instead of the column as a whole. For example, in the EXAMS table, we could find the
average mark obtained by the students by:

SELECT AVG(MARK)
FROM EXAMS ;

AVG(MARK)
---------
55

SQL Tips

SQL Server allows the COMPUTE clause which is used to calculate subtotals of
subtotals.

This value is not very informative as the exams were sat by students of all abilities. It
would be more meaningful to get the average mark for each student. This can be
obtained by using the GROUP BY clause:

SELECT STUDENT_NO, AVG(MARK)
FROM EXAMS
GROUP BY STUDENT_NO ;

STUDENT_NO AVG(MARK)
----------- ----------
1 62

2 52

3 70

4 42

5 55

6 74

7 45

8 62

This query first groups the rows in the EXAMS table by the values in STUDENT_NO.
The AVG() function then operates on each group. The average values output are thus
the averages for the exams taken by individual students.

Queries using the GROUP BY clause are known as grouped queries. All the rules for
using the ANSI/ISO functions that we have looked at also apply to grouped queries.
The only difference being that in grouped queries, the DBMS applies the functions to
each group individually rather than to the column as a whole. You can also get the
same results by running several queries with a WHERE clause. For example, to find the
average mark for a student:

FROM EXAMS

----------- ---------
1 65

By changing the 1 value in the predicate, we could calculate the average for different
students.

GROUP BY can be used with multiple fields. For example, in the SUBJECTS table, to
find the highest pass mark for each department/credits combination:

SELECT SUB_NAME, DEPT_NO, CREDITS, MAX(PASS)
FROM SUBJECTS
GROUP BY DEPT_NO, CREDITS ;

SUB_NAME DEPT_NO CREDITS MAX(PASS)
---------------- ------- ------- ---------
Engineering Drwg 1 1 71
Mathematics 1 2 65
Electronics 1 3 71

English Lit 2 1 60

Basic Accounts 3 1 67
Marketing 3 2 56

Industrial Law 4 2 52

Organic Chemistry 5 3 57

Anatomy 6 1 74
Physiology 6 3 78

SQL Tips

SQL Server's COMPUTE clause produces non-table results which are, needless to
say, highly non-standard.

4.4.6 Eliminating groups of data: The HAVING clause.

You cannot use aggregate functions in the WHERE clause of a SELECT statement.
This means that you cannot use WHERE to selectively eliminate data that does not
interest you from the results of aggregate queries. For example, in the query that we
used to find the average mark for each student, if we are only interested in averages
that are above 56%, then SQL won't let you use the following query because it uses
AVG() in the WHERE clause:

FROM EXAMS
WHERE AVG(MARK) > 56
GROUP BY STUDENT_NO ;

Error 67: Aggregate function used in WHERE.

The HAVING clause performs a similar function to WHERE in that it eliminates groups

from the results table. Thus to list only those students where the average is above 56%:

FROM EXAMS
GROUP BY STUDENT_NO
HAVING AVG(MARK) > 56 ;

----------- ----------
1 62

3 70

6 74

8 62

The field referenced by HAVING can not have more than one value for each group.
This means that in practice HAVING can only reference aggregate functions and
columns that are used in GROUP BY.

4.5 "Retrieving data from multiple tables":
SQL joins.

So far, we've been looking at queries that retrieve data from single table at a time.
Single table queries are useful but they do not exploit the full power of the SQL
language. SQL is a relational database query language and as such, one of it's most
important features is it's ability to retrieve information from several different related
tables. In relational database terms, this process is called a join. The tables to be joined
are named in the FROM clause of the SELECT with each table name separated by a
comma. The relationships between the tables in a join are defined by the predicate in
the WHERE clause. The predicate can refer to any column from the joined tables to
form the relations. For example, to list the names of all the lecturers and the subjects
that they teach:

SELECT LECTURERS.SURNAME, SUBJECTS.SUB_NAME
FROM LECTURERS, SUBJECTS
WHERE LECTURERS.DEPT_NO = SUBJECTS.DEPT_NO ;

LECTURERS.SURNAME SUBJECTS.SUB_NAME
------------------ ---------------------
Jones Electronics
Jones Engineering Drwg
Jones Mathematics
Scrivens Marketing

Scrivens Basic Accounts
Nizamuddin Marketing
Nizamuddin Basic Accounts
Campbell Organic Chemistry
Ramanujan Industrial Law
Finley Industrial Law

Of course, this join assumes that all the lecturers are multi-skilled in that each is able to
teach all the subjects in one particular department. Notice the column naming
convention we have used The column names in this query are prefixed by the name of
the table that the column is part of. If all the columns in the joined tables had unique
names, then the table prefix would not have been required. In our university example
though, there is a column called DEPT_NO in both the LECTURERS and the
SUBJECTS tables. In this case we must use LECTURERS.DEPT_NO and
SUBJECTS.DEPT_NO to distinguish between the columns. Generally, it is good to get
into the habit of using the table name prefix to specify columns. As your queries get
more and more complex, it may not always be clear to the reader which column you
mean if the table prefix is not used.

In the last query we did not have to tell SQL how to retrieve the data from the tables,
instead, we merely specified what data we wanted to see. The actual tables themselves
might have been stored on disks located at different sites. SQL shields the user from
these technicalities in that you do not have to know how to get at the data or even
where it is. You only have to specify the data to get at. When processing a query with a
join, SQL looks at all the possible combination of rows from the tables in the join and
uses the criteria defined in the predicate to add or omit the rows from the results table.
The steps involved in processing this query are shown in Figure 4.5.

SURNAME SUB_NAME
------- --------
Jones Mathematics----------> Add to Results
Jones English Lit
Jones Engineering Drwg-----> Add to Results
Jones Basic Accounts
.. ..
.. ..
Jones Electronics----------> Add to Results
Jones Marketing
Scrivens Mathematics
Scrivens English Lit
.. ..
.. ..
Scrivens Marketing-----------> Add to Results
.. ..
.. ..
Finley Marketing

----------> = The predicate is true for this row combination.

1. Construct a list of every possible combination of rows from the
LECTURERS and the SUBJECTS table.

2. Check to see if the predicate is true for each combination of rows.
ie. if LECTURERS.DEPT_NO = SUBJECTS.DEPT_NO.

3. If the predicate is true, then add the LECTURERS.SURNAME and the
SUBJECTS.SUB_NAME value for the row to the results table.

4. When all the combination rows have been checked, display the
results table.

Figure 4.5

We saw in the simple query, how we can use the asterisk character to mean "all the
columns". This also applies to queries involving table joins. The following query lists all
the columns of the joined tables:

SELECT *
FROM LECTURERS, SUBJECTS
WHERE LECTURERS.DEPT_NO = SUBJECTS.DEPT_NO ;

------- ----- ------- ------ ------ ----- --- ------
Jones R A 1 1 2 E 24000 03-25-1990
Jones R A 1 1 2 E 24000 03-25-1990
Jones R A 1 1 2 E 24000 03-25-1990
Scrivens T R 2 3 1 D 31800 09-30-1986
Scrivens T R 2 3 1 D 31800 09-30-1986
Nizamuddin W M 3 3 4 A 86790 05-26-1969
Nizamuddin W M 3 3 4 A 86790 05-26-1969
Campbell J G 4 5 3 C 43570 02-23-1980
Ramanujan S 5 4 5 C 40900 01-01-1985

The asterisk causes all the columns of both joined tables to be listed but since the
screen is only 80 columns wide, only those columns that fit on the screen are shown in
the above example. The asterisk is not usually used as it retrieves too much irrelevant
information. When joining tables, we are only interested in columns that convey useful
information that is directly related to the query.

The last query established a join between the LECTURERS table and the SUBJECTS
table through the use of columns which have the same data type in both tables, ie. the
LECTURERS.DEPT_NO and the SUBJECTS.DEPT_NO columns. In relational
databases, certain linkages are defined when the tables are first created, the primary
key/foreign key relationships for example. Joins can easily use these "natural"
relationships to extract data from tables. For example, the DEPT_NO column is the
primary key in the DEPARTMENTS table and a foreign key in the SUBJECTS table
which refers to DEPARTMENTS. So we can join these two tables using this column:

SELECT SUBJECTS.SUB_NAME, DEPARTMENTS.DEPT_NAME
FROM SUBJECTS, DEPARTMENTS
WHERE SUBJECTS.DEPT_NO = DEPARTMENTS.DEPT_NO ;

SUBJECTS.SUB_NAME DEPARTMENTS.DEPT_NAME
--------------------- ---------------------
Mathematics Engineering
English Lit Arts & Humanities
Engineering Drwg Engineering
Basic Accounts Management Studies
Industrial Law Industrial Law
Organic Chemistry Physical Sciences

Physiology Medicine
Anatomy Medicine
Electronics Engineering
Marketing Management Studies

Each subject is listed along with the department that offers it. Notice that we did not
specify the DEPT_NO field in the SELECT list. We only used DEPT_NO in the
predicate to from a link between the two tables. In practice, primary and foreign key
columns seldom appear in the results table because they are often just sequential
numbers or a combination of numbers and letters that do not mean very much to the
reader. The associated columns in the record that the key identifies convey far more
information eg. the SURNAME, DEPT_NAME, BUDGET etc.

You can also extend the join to more than two tables. For example, If we modify the
previous query to include the names of the lecturers that teach the course, we would be
joining three tables:

SELECT SUBJECTS.SUB_NAME, DEPARTMENTS.DEPT_NAME, LECTURERS.SURNAME
FROM SUBJECTS, DEPARTMENTS, LECTURERS
WHERE SUBJECTS.DEPT_NO = DEPARTMENTS.DEPT_NO

SUBJECTS.SUB_NAME DEPARTMENTS.DEPT_NAME LECTURERS.SURNAME
-------------------- ---------------------- -----------------
Mathematics Engineering Jones
Engineering Drwg Engineering Jones
Basic Accounts Management Studies Nizamuddin
Basic Accounts Management Studies Scrivens
Industrial Law Industrial Law Finley
Industrial Law Industrial Law Ramanujan
Organic Chemistry Physical Sciences Campbell
Electronics Engineering Jones
Marketing Management Studies Nizamuddin
Marketing Management Studies Scrivens

Notice that certain subjects (such as Basic Accounts) appear twice in the results with
different lecturer names. This is because lecturers who are in the same department
such as Nizamuddin and Scrivens have the same DEPT_NO value and both match the
SUBJECTS.DEPT_NO value for that subject row. So SQL lists the subject twice with
different lecturers.

When you join tables with a predicate such as LECTURERS.DEPT_NO =
SUBJECTS.DEPT_NO, NULL values for the DEPT_NO column (in both tables) will be
omitted from the results. A lot of commercial SQL implementations use a non-ANSI/ISO
standard technique called the outer join to include NULLs in the results. This is beyond
the scope of this book and we will not be discussing it.

SQL Tips

The IBM SQL products only support the inner join but many implementations
including SQL Server, Oracle and SQLBase support both the inner and the outer
joins.

4.5.1 Classification of joins.

SQL joins are classified according to the type of predicate that they use. All the joins
that have been described so far have used the equivalence operator (the = sign) in the
predicate eg. SUBJECTS.DEPT_NO = DEPARTMENTS.DEPT_NO. This type of join is
called the equijoin and is the one most commonly used. Any of the other comparison
operators can also be used in defining the predicate, and will lead to non-equijoins.

SQL Tips

The ANSI/ISO standard specifies only the inner join.

4.6 "Joining a table to itself":
The self-join.

SQL's concept of joining two or more tables also applies to joining two copies of the
same table. At first, this may sound strange. Surely the idea behind the join is to extract
information from related but different tables. What information can we extract by joining
two copies of the same table? Well, joining a table to itself, called the self-join enables
us to perform queries that exploit relationships within the table itself. Data retrieved by
self-joins cannot be obtained by any other type of query.

The rules governing the self-join are the same as for any other type of join. In fact if we
think about it, a self-join is just like any other join but are where all the joined tables are
identical. This last fact does present some problems as we shall see.

As an example, consider the LECTURERS table. If we wanted to list all pairs of
lecturers who work in the same department (ie. have the same DEPT_NO column
value), we could only do this by using a self-join query:

SELECT LECTURERS.SURNAME, LECTURERS.SURNAME
FROM LECTURERS, LECTURERS
WHERE DEPT_NO = DEPT_NO ;

LECTURERS.SURNAME LECTURERS.SURNAME
------------------ -----------------
Jones Jones

Wait a minute. Let's look at this query again. Although it is syntactically correct, it
doesn't make sense. It is clear that the query is trying to join two copies of the
LECTURERS table, but it's not clear which DEPT_NO column is from which table. In
the last section, we learnt that when you join two tables where column names are
repeated, you had to use the table name prefix to fully identify each column. When
using the self-join, we are faced with the added problem of repeated table names.
Fortunately, SQL allows us to use aliases or temporary names for tables. If we re-write
the query using aliases, it will become obvious what aliases are and how to use them:

SELECT F.SURNAME, S.SURNAME, F.DEPT_NO
FROM LECTURERS F, LECTURERS S
WHERE F.DEPT_NO = S.DEPT_NO ;

F.SURNAME S.SURNAME F.DEPT_NO
--------------- ---------------- ---------
Jones Jones 1
Scrivens Nizamuddin 3
Scrivens Scrivens 3
Nizamuddin Nizamuddin 3
Nizamuddin Scrivens 3
Campbell Campbell 5
Ramanujan Finley 4
Ramanujan Ramanujan 4
Finley Finley 4
Finley Ramanujan 4

When executing this query, the DBMS treats the aliases as two distinct tables and joins
them accordingly. The rows from the joined table are checked against the predicate and
where F.DEPT_NO = S.DEPT_NO, they are retrieved into the results table. The FROM
clause of the query tells SQL that the first incarnation of LECTURERS is to be known by
the alias F and the second incarnation, by the alias S throughout the duration of the
query. This makes life a lot easier. In the SELECT clause, the SURNAME columns are
described as F.SURNAME and S.SURNAME. We used the aliases as the table name
prefixes for these columns because in the self-join, both table names are the same. In
the WHERE clause, the alias names are again used as table name prefixes to specify
the DEPT_NO columns from the first and the second copies of the LECTURERS table.
SQL allows the use of alias names for tables in all queries, not just self-joins. So for

example if your database consisted of tables with long names, then you could define
and use simple aliases to refer to them instead. You must remember though that the
alias only exists for as along as the query is being executed but most commercial
implementations allow you to define more permanent aliases for tables called
synonyms. Oracle for example lets you use the CREATE SYNONYM statement to
assign a permanent alias to a table.

The results of the previous self-join query contain redundant data. For example, the first
row lists Jones twice. We are only interested in pairs of different lecturers who work in
the same department. The first row only lists one lecturer, Jones as being in department
1. To eliminate such redundancy, we need to add an extra condition to the WHERE
clause:

WHERE F.DEPT_NO = S.DEPT_NO
AND F.SURNAME <> S.SURNAME ;

--------------- --------------- ---------
Nizamuddin Scrivens 3
Ramanujan Finley 4
Finley Ramanujan 4

Although this last condition gets rid of some of the redundant rows, the remaining pairs
of values are still listed twice. eg. Scrivens with Nizamuddin in one row, and Nizamuddin
with Scrivens in another. Such repetition is usually eliminated by using > or < instead of
<> in the extra condition of the WHERE clause:

WHERE F.DEPT_NO = S.DEPT_NO
AND F.SURNAME > S.SURNAME ;

--------------- ---------------- ---------
Ramanujan Finley 4

4.7 "Nested SELECT statements":
The subquery.

We've seen how queries work and we've seen how predicates work. In this section we
will be looking at how to use queries in the predicates of other queries.

Recall that a predicate defines a condition which is tested against the rows of the
table(s) from which data is to be retrieved. All those rows which make the predicate
condition true are retrieved in the results. A subquery can also be used to provide one
or more of the values that are used in the predicate. For example, consider this
situation, which is quite common in live SQL databases. We want to look at the records
of all the exams taken by Phil J M A Ayton. Although we know the student's full name,
we do not know his student number. This is where the subquery comes in:

SELECT *
FROM EXAMS
WHERE STUDENT_NO =
(SELECT STUDENT_NO
FROM STUDENTS
WHERE SURNAME = 'Ayton') ;

--------- ----------- ----- -----------
2 3 89 06-08-1984
2 3 51 05-11-1984

The DBMS executes the subquery first. This generates a single value of 3 for the
STUDENT_NO column from the row where SURNAME is equal to 'Ayton'. The DBMS
then evaluates the full query as usual. The predicate being set to STUDENT_NO = 3.

When using the equivalence operator (=) in the predicate, you must make sure that the
subquery retrieves exactly one value. This means that the subquery must select only
one column in the SELECT list and must be phrased so that it retrieves a single row.
The column selected by the subquery must also be of the same data type as the
column it is being compared to in the predicate. If these conditions are not met, then
SQL will signal an error and the query will be aborted. The following query contains a
subquery which selects more than one row and SQL rejects it:

SELECT *
FROM EXAMS
WHERE STUDENT_NO =
(SELECT STUDENT_NO
FROM STUDENTS
WHERE DEPT_NO = 3) ;

Error 76: The subquery found more than
one value.

And this variation of the same query selects no rows, and also fails:

SELECT *
FROM EXAMS
WHERE STUDENT_NO =
(SELECT STUDENT_NO
FROM STUDENTS

Error 75: The subquery did not find
any values.

Aggregate functions are allowed in the subquery as long as they do not use the
GROUP BY or the HAVING clauses. The reason for this is that aggregate functions on
their own operate on the whole column and produce a single value as output. When
used with GROUP BY and HAVING, aggregate functions operate on subsets of values
in the column and produce one value per group as output. Even if you phrased the
subquery with GROUP BY so that the HAVING clause retrieves only one value as
output, the query will still be rejected by most SQL systems. You can usually get round
this restriction by judicious use of the WHERE clause in the subquery. Let's look at an
example of a subquery which uses an aggregate function to get the names of those
lecturers who earn less than the average pay for all the lecturers:

SELECT SURNAME, PAY
FROM LECTURERS
WHERE PAY <
(SELECT AVG(PAY)
FROM LECTURERS) ;

SURNAME PAY
---------------- -------
Nizamuddin 86790
Campbell 43570

The ANSI/ISO standard requires that the format of the predicate with subquery cannot
change. The subquery always appear after the comparison operator and cannot appear
before. Thus, we cannot re-arrange the previous query to read:

SELECT *
FROM EXAMS
WHERE (SELECT STUDENT_NO

FROM STUDENTS
WHERE DEPT_NO = 3)
= STUDENT_NO ;

Error 36: Invalid Syntax.

This means that you sometimes have to reverse the logic of the statement without
changing the meaning to convert it into a form that SQL can accept.

So far, we've have looked at using subqueries in predicates with comparison operators
(=, <>, >, <, >=, <=). These queries necessarily required the subquery to output a single
value. You cannot say DEPT_NO = 12, 14, 7, 9 for example as it doesn't make sense.
To use subqueries which return multiple values, you must use the IN operator. For
example, to look at all the exams taken by students in department number 3:

SELECT *
FROM EXAMS
WHERE STUDENT_NO IN
(SELECT STUDENT_NO
FROM STUDENTS

-------- ---------- ---- ----------
2 3 89 06-08-1984
2 3 51 05-11-1984
5 8 52 05-20-1984

The previous version of this query failed because the subquery retrieved more than one
row. The difference here is that the equivalence operator (=) has been replaced by the
IN operator. IN looks for a matching value from the rows that are retrieved by the
subquery. Although IN can deal with multiple values retrieved by the subquery, the
values must all come from the same field. This means that you must still specify a
single column in the subquery's SELECT clause and the column must have the same
data type as the value that it is being compared to.

We can reverse the logic of IN with NOT IN. For example, in the previous query, we can
retrieve the exams taken by students who are not in department number 3 by using
NOT IN:

SELECT *
FROM EXAMS
WHERE STUDENT_NO NOT IN
(SELECT STUDENT_NO
FROM STUDENTS

-------- ----------- ---- ----------
1 1 76 05-23-1984
9 1 42 05-20-1984
3 1 67 05-15-1984
2 2 52 06-05-1984
4 4 34 05-11-1984
10 4 49 06-26-1984
5 5 62 05-03-1984
5 6 70 05-17-1984
5 7 36 05-23-1984
6 9 67 05-15-1984
6 10 82 06-05-1984
6 11 73 06-08-1984
7 12 27 05-11-1984
8 12 56 05-11-1984
8 13 67 06-26-1984
7 13 63 05-03-1984

Because IN uses a set of values to match against, it can also be used in place of the
equivalence operator (=). This means that we can rephrase one of our earlier queries
as:

SELECT *
FROM EXAMS
WHERE STUDENT_NO IN
(SELECT STUDENT_NO
FROM STUDENTS
WHERE SURNAME = 'Ayton') ;

-------- ---------- ---- ----------
2 3 89 06-08-1984
2 3 51 05-11-1984

Here, we've simply replaced the equivalence operator (=) with IN but the query still
retrieves exactly the same rows. You might well ask why bother with the equivalence
operator at all? Why not use the IN operator all the time? The answer is we could, but
the equivalence operator is very useful in highlighting cases where potential errors
could affect the results. In the above query for example, if there were two students with
the surname Ayton, then the query with IN would have retrieved the exam results for
both of them. Looking at the results, you would mistakenly think that these exams were
taken by the same person. The version of the query, which used the equivalence
operator, would have simply failed if the subquery retrieved two rows for Ayton.

When we were discussing aggregate functions, remember we said that the value

supplied to the HAVING clause can also be generated by a subquery. Let's look at an
example where this is done:

SELECT SUB_NO, SUB_NAME, AVG(MARK)
FROM EXAMS
GROUP BY SUB_NO
HAVING SUB_NO =
(SELECT SUB_NO
FROM SUBJECTS
WHERE PASS = 60) ;

SUB_NO SUB_NAME AVG(MARK)
------ -------- ---------
2 English Lit 64

This query calculates the average mark obtained by students in the department whose
budget is 2,510,000.

The nested queries that we've used so far have all been second level. The ANSI/ISO
standard itself does not place any restriction on the number of levels of nesting that you
can have, but practical constraints limit nesting to quite a low number. Higher levels of
nesting require far greater processing and it becomes difficult for the reader to follow
what the query is trying to do. Many implementations of SQL restrict subquery nesting
to a low value. You can usually phrase all your queries to fit this level of nesting.

4.8 "Linked SELECT statements":
The correlated subquery.

We have seen how you can link two or more tables in a single query by using the SQL
join operation. In this section, we will look at the correlated subquery. This is another
method of extracting data from different tables by linking them through the subquery. A
subquery becomes a correlated subquery when it refers to columns from the main
query's table. As you will see, correlated subqueries are similar to joins in that they both
involve comparing each row of a table against every row of another table. The similarity
does not end there. Just as we can join two copies of the same table, so we can also
correlate a table to itself.

An advanced warning. The concepts of correlated subqueries are probably the most
difficult in SQL for a beginner to understand. Don't worry too much if you find this
section a bit confusing on the first reading. As we've said before, the best teacher is
experience. Try the queries given for yourself, on your own SQL system. Vary the query
to see the different results and you will soon grasp the ideas behind correlated
subqueries by seeing and by doing.

So, what does a correlated subquery look like? Well, here's one:

SELECT *
FROM EXAMS
WHERE SUB_NO IN
(SELECT SUB_NO
FROM SUBJECTS
WHERE SUBJECTS.PASS <= EXAMS.MARK);

------ ---------- ---- ----------
1 1 76 05-23-1984
2 3 89 06-08-1984
5 5 62 05-03-1984
5 6 70 05-17-1984
5 8 52 05-20-1984
6 9 67 05-15-1984
6 10 82 06-05-1984
6 11 73 06-08-1984

This query retrieves rows from the EXAMS table for those students who pass in the
subject. The EXAMS table is in the outer query and the STUDENTS table in the
correlated subquery (some texts refer to the correlated subquery as the interblock
reference). The subquery uses the phrase WHERE SUBJECTS.PASS <=
EXAMS.MARK. This is the same as saying "where the student's mark is equal to more
than the pass mark for the subject" The use of the column from the table in the outer
query, the EXAMS.MARK column, in the subquery is known as an outer reference.
Although this query may be less efficient because of the interblock reference and can
probably be better expressed without using it, there are many SQL queries that cannot
be performed without the correlated subquery. The correlated subquery is executed
once for each row in the outer query. Since the value from the outer query changes for
each row, the inner query results will be different for each outer query row. The current
outer query row for which the subquery is executed is called the candidate row. Figure
4.6 shows the steps involved in executing this query.

As we said earlier the correlated subquery can also refer to two incarnations of the
same table (cf. the self-join). For example:

SELECT DISTINCT A.DEPT_NO
FROM LECTURERS A
WHERE A.DEPT_NO IN
(SELECT DEPT_NO
FROM LECTURERS B
WHERE B.SURNAME <> A.SURNAME) ;

A.DEPT_NO
---------
3
4

This query lists the department numbers for the departments that have more than one
lecturer on staff. SQL runs the subquery once for each A.DEPT_NO (each outer query
row). The subquery checks if there is another lecturer who is also in the same
department. (it uses the SURNAME field to differentiate between them) This query
illustrates the importance of the correlated subquery. It is impossible to perform this type
of query without use of the correlated subquery.

SELECT * FROM EXAMS WHERE SUB_NO IN (SELECT SUB_NO FROM SUBJECTS WHERE
SUBJECTS.PASS <= EXAMS.MARK) ;
/|
|
----- This is the outer reference.

------ ---------- ---- ----------
1 1 76 23-MAY-1984 --------
9 1 42 20-MAY-1984 |
~~ ~~ ~~ ~~~~ | Candidate Row
~~ ~~ ~~ ~~~~ |
|/
The subquery is executed using the
value of 76 for the outer reference.

SUB_NO The subquery builds a set
------ of values for which the
1 predicate SUBJECTS.PASS <= 76
2 is true.
3 |
4 |
5 <-----------------------------------
6
8
9
10

------ ---------- ---- ----------
1 1 76 23-MAY-1984 <----- Test the predicate of the main query against the intermediate set
of SUB_NO values. It is true, so this row is added to the results table.

------ ---------- ---- ----------
1 1 76 23-MAY-1984
2 3 89 08-JUN-1984 <--- The subquery is run for
5 5 62 03-MAY-1984 each row of the outer
5 6 70 17-MAY-1984 table in turn, and this
5 8 52 20-MAY-1984 produces the full
6 9 67 15-MAY-1984 results table.
6 10 82 05-JUN-1984
6 11 73 08-JUN-1984

Figure 4.6

4.9 "Does the subquery retrieve values":
The EXISTS operator.

The EXISTS operator is used in the predicate of a query just like the IN operator.
EXISTS must always have a subquery as its argument and it returns true if the
subquery retrieves any values. EXISTS returns false if the subquery does not retrieve
any values. As a simplified example consider the following query. It retrieves all the
rows in the SUBJECTS table only if there is a subject which has a pass mark of 75% or
more:

SELECT *
FROM SUBJECTS
WHERE EXISTS
(SELECT *
FROM SUBJECTS
WHERE PASS >= 75) ;

------- --------- -------- -------- -----
7 Physiology 6 3 78

Granted that this query does not make too much sense in the real world, but it does
serve to illustrate the use of EXISTS. SQL executes the subquery and finds that there is
only one row, the row for Physiology, for which the pass mark is greater than 75%.
Because the subquery found a row, EXISTS evaluates to true for all rows in the outer
table. The subquery does not make any reference to the outer table columns so it is
only run once and not once for each row of the sub-query. The query will thus retrieve
all the rows from the SUBJECTS table. Notice that the subquery uses the asterisk in the
SELECT clause. This is because EXISTS only checks if output is produced by the
subquery. It doesn't care what actual columns are selected or returned.

EXISTS can also be used with correlated subqueries. With these, the EXISTS clause is
evaluated separately for each row of the outer query table. EXISTS will return true or
false depending on the value of each row in the outer query table unlike in the previous
example where the subquery was only evaluated once. The following query uses a
correlated subquery with EXISTS. It lists the student number for those students who
have sat more than one exam:

SELECT DISTINCT STUDENT_NO
FROM EXAMS A
WHERE EXISTS
(SELECT *
FROM EXAMS B
WHERE B.STUDENT_NO = A.STUDENT_NO
AND B.SUB_NO <> A.SUB_NO) ;

STUDENT_NO
----------
1
4
12
13

For each outer query row, the subquery searches the EXAMS table to find rows where
the student numbers in both the outer and the inner query are the same. The AND
clause eliminates those cases where the student sat more than one exam in the same
subject. The DISTINCT keyword is used in the outer query because without it, the query
would have listed each student number more than once (once for each exam that they
took).

The EXISTS examples that we have seen so far have been simple queries with
subqueries. EXISTS can also be applied to queries where the outer query joins tables.
For example, if we wanted to extend the previous query so that it displayed the
student's name as well as the student number, we would have to use a query which
joined the EXAMS and the STUDENTS tables:

SELECT DISTINCT A.STUDENT_NO, B.SURNAME
FROM EXAMS A, STUDENTS B
WHERE EXISTS
(SELECT *
FROM EXAMS C
WHERE C.STUDENT_NO = A.STUDENT_NO
AND C.STUDENT_NO = B.STUDENT_NO
AND C.SUB_NO <> A.SUB_NO) ;

A.STUDENT_NO B.SURNAME
------------ -----------------
1 Duke
4 Patel
12 Hung-Sun
13 Middleton

The outer query joins the EXAMS and the STUDENTS tables. The extra AND clause
(AND C.STUDENT_NO = B.STUDENT_NO) in the inner query ensures that the
subquery only retrieves rows where the STUDENT_NO value from the STUDENTS
table matches the STUDENT_NO value from the EXAMS table. This in turn ensures
that the right SURNAME value is listed against each STUDENT_NO value in the results
table.

The meaning of EXISTS is reversed by adding the NOT boolean operator. Thus the

following query lists the student numbers of those students who sat only one exam:

SELECT DISTINCT STUDENT_NO
FROM EXAMS A
WHERE NOT EXISTS
(SELECT *
FROM EXAMS B
WHERE B.STUDENT_NO = A.STUDENT_NO
AND B.SUB_NO <> A.SUB_NO) ;

STUDENT_NO
----------
2
3
5
6
7
8
9
10
11

4.10 "Two more subquery operators":
The ANY and ALL operators.

We have looked at the IN operator and we've also looked at the EXISTS operator. Now
let's examine the last two specialized operators used specifically with subqueries. The
ANY (also called SOME which is synonymous with ANY) and ALL operators differ from
EXISTS in that they can be used with relational operators.

SQL Tips

The ANSI/ISO standard specifies that SOME and ANY can be used
interchangeably.

The ANY operator evaluates to true if any of the values retrieved by the subquery equal
the outer query column value used in the predicate. For example the following query
retrieves the names of the lecturers who work in a department which has a budget of
more than 3,000,000:

SELECT SURNAME, INITL, DEPT_NO
FROM LECTURERS A
WHERE A.DEPT_NO = ANY

(SELECT B.DEPT_NO
FROM DEPARTMENTS B
WHERE BUDGET > 3000000) ;

SURNAME INITL DEPT_NO
--------------- ----- -------
Jones R A 1

As with the IN and the EXISTS operators, the ANY clause also requires a subquery
which must be an entire SELECT statement. In this example the result of the subquery
is a list of B.DEPT_NO values. SQL then tests if the value of A.DEPT_NO for the
current row is equal to ANY of the values retrieved by the sub-query. If it is, then the =
ANY clause returns true.

The = ANY phrase produces the same results as the IN operator. As well as =, ANY
can also be used with the other valid SQL comparison operator (=, <, <=, >, >=, <>).
We could have used > in the previous query:

FROM LECTURERS A
WHERE A.DEPT_NO > ANY
(SELECT B.DEPT_NO
FROM DEPARTMENTS B

--------------- ----- -------
Scrivens T R 3
Nizamuddin W M 3
Campbell J G 5
Ramanujan S 4
Finley G Y 4

At first you would think that this query would also retrieve the records of those lecturers
who work in departments with budgets more than 3,000,000. Closer examination revels
that the query actually retrieves the rows of those lecturers who work in a department
which has a department number more than 1. The row for Jones has been omitted as
he works in a department where the department number equals 1. As before, the
subquery selects the DEPT_NO values for departments with a budget of more than
3,000,000, ie. 1, 5 and 6. The outer query predicate returns true for those rows where
the A.DEPT_NO value is greater than any one of 1, 5, 6. This is true for all the lecturers
except Jones. In general, > ANY means greater than the smallest value in the list
produced by the subquery, and < ANY means less than the largest value produced by
the subquery.

Note that ANSI/ISO SQL allows you to use the SOME keyword in place of ANY. They
both produce exactly the same results. Thus the previous query could have been
written as:

FROM LECTURERS A
WHERE A.DEPT_NO > SOME
(SELECT B.DEPT_NO
FROM DEPARTMENTS B

--------------- ----- --------
Scrivens T R 3
Nizamuddin W M 3
Campbell J G 5
Ramanujan S 4
Finley G Y 4

The versatility of the SQL language means that there is usually more than one way of
expressing any query. All queries which use the ANY operator for example, can also be
constructed with the EXISTS operator (the reverse is not true though). The query to list
lecturers who work in a department with a budget of more than 3,000,000 can thus be
expressed using EXISTS as:

FROM LECTURERS A
WHERE EXISTS
(SELECT *
FROM DEPARTMENTS B
WHERE BUDGET > 3000000
AND B.DEPT_NO = A.DEPT_NO) ;

--------------- ----- -------
Jones R A 1
Campbell J G 5

The EXISTS version of the query is less efficient in terms of the processing it requires.
The reason for this is that it's correlated subquery must be executed once for each of
the rows in the outer table. The ANY version of this query only executes the subquery
once. The values produced by the subquery, are then used for all the rows of the outer
table.

The ALL operator returns true if all the values selected by the subquery meet the
requirements defined by the predicate. The ALL keyword is used in an SQL query just

as the ANY keyword. For example, the following query lists the names of those
lecturers who do not teach Industrial Law:

SELECT SURNAME, INITL
FROM LECTURERS A
WHERE A.SUB_NO <> ALL
(SELECT B.SUB_NO
FROM SUBJECTS B
WHERE SUB_NAME = 'Industrial Law') ;

SURNAME INITL
--------------- -----
Jones R A
Scrivens T R
Nizamuddin W M
Campbell J G

SQL executes the subquery first. This produces a SUB_NO value of 5 for the Industrial
Law subject. The <> ALL condition matches all the outer table rows where A.SUB_NO
is not equal to 5. This leaves us with a list of lecturers who do not teach Industrial Law.
Note that if the subquery had produced more than one value, then the <> ALL would
have made the predicate true only for those rows where A.SUB_NO is not equal to all
the subquery values. The equivalence operator (=) is not usually used with ALL
because = ALL would only make sense if all the values produced by the subquery are
identical (A.SUB_NO cannot equal 5 and also 8 at the same time).

Sometimes, the subquery produces no values. In these cases, SQL sets the ANY
operator to false for all rows of the outer query, and sets ALL to true for all outer query
rows. Thus if we wanted to list those lecturers who earn more than all those in
department number 12:

SELECT SURNAME, INITL, PAY
FROM LECTURERS A
WHERE A.PAY > ALL
(SELECT PAY
FROM LECTURERS B

SURNAME INITL PAY
--------------- ----- -------
Jones R A 24000
Scrivens T R 31800
Nizamuddin W M 86790
Campbell J G 43570
Ramanujan S 40900
Finley G Y 34210

As there are no lecturers in department 12, the subquery comes back empty. This
means that the ALL predicate is true for all rows. Thus the query lists all the lecturers
because they all earn more then the no-existent lecturers of department 12. Similarly, if
we had used ANY instead of ALL:

SELECT SURNAME, INITL, PAY
FROM LECTURERS A
WHERE A.PAY > ANY
(SELECT PAY
FROM LECTURERS B


The ANY predicate is now false for all rows. So this query retrieves no rows.

4.11 "Combining multiple queries":
The UNION clause.

The UNION clause allows you to combine the output of two or more individual queries.
UNION differs from subqueries in that it is made up of queries that are independent
from each other. UNION combines the output of these individual SELECTs and lists
them as part of a single output table. For example, to get a list of all students and
lecturers in department number 3:

FROM STUDENTS
WHERE DEPT_NO = 3

UNION

FROM LECTURERS
WHERE DEPT_NO = 3 ;

SURNAME DEPT_NO
--------------- -------
Ayton 3
Brown 3
Mulla 3
Scrivens 3
Nizamuddin 3

Notice that the output columns don't have column headings. This is because the

columns values are from two separate tables which may have different headings (in this
case they don't). Figure 4.7 shows how SQL executes this query. The UNION is made
up of two queries, one lists the students in department 3 and the other lists the
lecturers.

SQL Tips

Some commercial systems, including SQL Server and dBase IV do not support the
UNION operation.

------- ---------- ----- ---------- ------- ----
Duke Fitzroy 11-26-1970 1 4 2

~~~ ~~~ ~~~ ~~~ ~~~ ~~~
~~~ ~~~ ~~~ ~~~ ~~~ ~~~

Layton Hugh 11-16-1971 15 5 1
Wickes Wendy Y Y W 12-05-1969 16 1 1

THE STUDENTS TABLE

FROM STUDENTS WHERE DEPT_NO = 3
|
| SQL executes the first query and internally stores
| the results.
|/

SURNAME DEPT_NO
------- -------
Ayton 3
Brown 3
Mulla 3
Kitson 3
Grace 3
Avery 3
Davis 3
TABLE A

Figure 4.7

------- ----- ------- ------- ----- ----- --- ------
Jones R A 1 1 2 E 24000 03-25-1990

~~~ ~~~ ~~~ ~~~ ~~~ ~~~ ~~~ ~~~
~~~ ~~~ ~~~ ~~~ ~~~ ~~~ ~~~ ~~~

Finley G Y 6 4 5 D 34210 03-28-1960

THE LECTURERS TABLE

FROM LECTURERS WHERE DEPT_NO = 3
|
| SQL executes the second query and internally stores
| the results.
|/
|
SURNAME DEPT_NO
------- -------
Ayton 3
Brown 3
Mulla 3
Kitson 3
Grace 3
Avery 3
Davis 3
TABLE B

SQL internally combines results tables A and B and outputs the results as a
UNION of these tables.

------- -------
Ayton 3
Brown 3
Mulla 3
Kitson 3
Grace 3
Avery 3
Davis 3
Scrivens 3
Nizamuddin 3

Figure 4.7 … Continued

The ANSI/ISO standard applies some restrictions on the use of the UNION clause.
These include:

- The columns selected by the individual SELECT statements must be compatible.
ie. each query must select the same number of columns and each
corresponding column must have the same data type.

- If one column is specified as NOT NULL, then the corresponding column in the
other SELECT statements must also be NOT NULL.

- The UNION clause cannot be used in subqueries.

- The individual SELECT statements in the UNION must not use aggregate
functions.

- The individual SELECT statements must not use the ORDER BY clause.

The UNION will eliminate duplicate rows from the final results table by default. This is
the opposite of SELECT statements, where duplicate rows are included in the results by
default. You can instruct SQL to leave the duplicate rows in the results by using UNION
ALL instead of UNION.

Although you cannot use ORDER BY in the individual queries, you can specify ordering
on the results of the UNION itself. For example, to rephrase the previous query and
order the results alphabetically by surname:

FROM LECTURERS
WHERE DEPT_NO =
(SELECT DEPT_NO
FROM DEPARTMENTS
WHERE DEPT_NAME = 'Management Studies')

UNION

FROM STUDENTS
WHERE DEPT_NO =
(SELECT DEPT_NO
FROM DEPARTMENTS
WHERE DEPT_NAME = 'Management Studies')

ORDER BY 1 ;

SURNAME DEPT_NO
--------------- -------
Ayton 3
Brown 3
Mulla 3

Nizamuddin 3
Scrivens 3

The ORDER BY appears at the end of the UNION and acts on the results produced by
it. ORDER BY uses a column number to define the ordering sequence instead of a
column name because the results of a UNION query do not show column names. This
query also shows us that, we can use subqueries in the individual SELECT statements
of the UNION.

Chapter 5 ADDING AND UPDATING DATA.
SQL allows data to be added to, updated in and deleted from tables by using the
INSERT, UPDATE and DELETE Data Manipulation Language (DML) commands.
ANSI/ISO SQL refers to all these commands generically as the update commands and
this sometimes causes confusion because UPDATE is also a specific SQL command.
In this book, we will be using the word UPDATE to refer to the SQL command and
update to refer to the group of commands.

When you use any of the DML commands to manipulate the data in the database, the
DBMS must be capable of carrying out your request as well as similar requests from
other users of the system. This means that the DBMS must protect the overall integrity
of the database at all times, preventing the changes made by one user from interfering
with those made by other users on the system.

5.1 "Adding Single Rows at a Time":
The INSERT command.

Records are added to tables by using the INSERT command. Essentially, there are two
variations on this command. First, INSERT statements that add records a row at a time.
And second, INSERT statements that add several rows at a time.

The syntax of the single-row INSERT statement is shown in Figure 5.1. For example, to
insert the first row into the STUDENTS table:

INSERT INTO tbl_name
[ ( col_name { , col_name }* ) ]
VALUES ( value { , value }* ) ;

tbl_name
The name of the table to insert data into. This must have been
previously defined with the CREATE TABLE command.

col_name
The name of a table column to insert data into. If no column
names are mentioned, then it is assumed that data is to be
INSERTed for all the columns in the table.

value
The value to insert into the corresponding table column. CHAR
type data must be inside single-quotes. The data in the value
list must correspond to the column names specified in the column
list.

Figure 5.1

INSERT INTO STUDENTS
(SURNAME, FIRST_NAME, D_O_B, STUDENT_NO, DEPT_NO, YEAR)
VALUES ('Duke', 'Fitzroy', '26-NOV-70', 1, 4, 2);


Obviously, in order to be able to add data to a table, the table must have already been
created by using the CREATE TABLE command. The INSERT command does not
produce any output data. On most interactive SQL systems though, the DBMS tells you
if rows have been added, and if so, how many.

Data can only be INSERTED into tables which the user owns or has INSERT privilege
on. In practice, what this means is that you must have created the table or the person
who created it must give you permission to insert data by using the GRANT command.

The column list is optional in the INSERT statement. If a list of columns is specified,
then the values list must contain the same number of items and in the same order. The
data type of each column/value pair must also be compatible.

If no column names are mentioned, then it is assumed that data is to be INSERTed for
all columns. Thus the following is also a valid SQL statement:

INSERT INTO STUDENTS
VALUES ('Al-Essawy', 'Zaid M A', '26-NOV-70', 2, 4, 2);


This query also inserts a row into the STUDENTS table, but it does not specify a
column list. SQL assumes that data is to be added to all the columns in the table.

CHAR type data must be inside single-quotes, ' '. The DATE-TIME type is not defined
by ANSI/ISO so different SQL vendors have different specifications on how a DATE-
TIME value must be entered. Usually, it is entered as if it is a CHAR type, eg '01271990'
or '27-Jan-1990'.

You can enter NULLs as column values by using the NULL keyword in place of a
column value. In the SUBJECTS table for instance, if you don't know the credits that are
awarded for a subject, you could enter NULL for this column:

INSERT INTO SUBJECTS
VALUES (1, 'Mathematics', 1, NULL, 65 );


Columns will also be set to NULL (or the default value if one was defined in the
CREATE TABLE statement) if they are omitted from the column list. The previous
INSERT could also have been expressed as:

INSERT INTO SUBJECTS (SUB_NO, SUB_NAME, DEPT_NO, PASS)
VALUES (1, 'Mathematics', 1, 65 );


The CREDITS column is missing from the column list, so SQL sets the value in this
column to the default value. As we have not defined a default value for this column,
SQL enters a NULL for this column.

5.2 "Adding Multiple Rows at a Time":
The INSERT with SELECT command.

The INSERT command can be used to add more than one row at a time to a table if it is
used in conjunction with an appropriate SQL query. To do this, the VALUES clause of
the INSERT statement must be replaced with a SELECT statement that retrieves the
required rows from a second table. As an example, suppose we create a table called
ELITE_EXAMS which holds those exam results where students have scored 80% or
more. An easy way of populating this table would be to extract the rows from the
EXAMS table where the value for the MARK column is 80% or greater. After creating
the ELITE_EXAMS table, the following query will populate it:

INSERT INTO ELITE_EXAMS
SELECT * FROM EXAMS
WHERE MARK >= 80 ;

2 rows inserted.

In order for this INSERT to work, the ELITE_EXAMS table must have the same column
types as the EXAMS table and in the same order. Thus the first two columns of
ELITE_EXAMS must be INTEGER types with the third column being DECIMAL and the
fourth DATE. Once it is created and populated, the ELITE_EXAMS table is a database
entity in its own right. It is not related to the EXAMS table in any way except that it
shares some of the values of that table. So if the data in EXAMS changes, then SQL
does not pass the changes to ELITE_EXAMS.

The INSERT with SELECT statement can be used with column names if you wish to
move only selected columns:

INSERT INTO ELITE_EXAMS (E_MARK, E_STUDENT)
SELECT MARK, STUDENT_NO FROM EXAMS
WHERE MARK >= 80 ;

2 rows inserted.

This statement takes only the MARK and the STUDENT_NO columns from EXAMS. Of
course, in this case, only the E_MARK and the E_STUDENT columns from
ELITE_EXAMS will have valid values. SQL will enter NULLs for the other two columns.

In this section, we have so far seen how queries are used with the INSERT statement
to add data that already exists in other tables. The SQL update commands, namely
INSERT, UPDATE and DELETE, also allow the use of sub-queries as well as queries in
targeting rows that you are interested in. As an example consider if we created a table
called LOW_BUDGET which holds the records of those students who study in a
department with an annual budget of less than 100,000. The data that we need to
populate this table already exists in the university database. The following INSERT
selects qualifying rows from the STUDENTS table and adds them to the
LOW_BUDGET table:

INSERT INTO LOW_BUDGET
SELECT *
FROM STUDENTS
WHERE DEPT_NO IN
(SELECT DEPT_NO
FROM DEPARTMENTS
WHERE BUDGET < 100000) ;

4 rows inserted.

The query (along with the sub-query) sifts through the STUDENTS and the
DEPARTMENTS tables and finds the records of those students where the department
that they study in has a budget of less than 100000. It is important to note that the query
(or the sub-query) must not make any reference to the table that INSERT is operating
on, in our case, LOW_BUDGET. This constraint means that you cannot easily perform
updates based on information contained in the table that is going to be updated. In all
such cases, the desired update can be accomplished by using two queries. One, a
query to get the information from a table and the second to update the table based on
this information. Apart from this restriction, all the material described in the section on
queries and sub-queries is also applicable to queries and sub-queries used as part on
an INSERT statement.

5.3 "Modifying Data in Rows":
The UPDATE command.
The UPDATE command is used to change the existing values of the columns. In it's
simplest form UPDATE only needs three pieces of information: the name of the table
where updates are required, the name(s) of the column(s) to update and the value(s) to
set the column(s) to. You must have guessed by all the (s)'s flying around that UPDATE
can change the value of more than one column in a single statement.

In the LECTURERS table, for example, the following UPDATE will set the salary of all
the lecturers to 25,000:

UPDATE LECTURERS
SET PAY = 25000 ;

6 rows updated.

When updating rows, we usually do not want to use such a wide brush as to change the
column values of all the rows in the table at once. UPDATE can be qualified with an
optional WHERE clause which can specify a group of rows to modify.

In the LECTURERS table for example, if Jones had served long enough to be promoted
to grade D seniority, we could change his record by:

UPDATE LECTURERS
SET GRADE = 'D'
WHERE LECT_NO = 1 ;

1 row updated.

If Jones also got a pay rise to go with his promotion, then we could have modified these
two columns with a single UPDATE statement:

UPDATE LECTURERS
SET GRADE = 'D', PAY = 28000
WHERE LECT_NO = 1 ;

1 row updated.

Although the UPDATE statement allows you to modify several columns in a table, you
cannot update multiple tables with a single command. This follows on from the fact that
table prefixes cannot be used with the column names in the SET clause.

Scalar expressions can be used in the SET clause as a multiplication factor for
example. This is useful in situations where you need to change the values of a column
by a preset amount. In the LECTURERS table for example, if it is university policy to
award a set percentage pay increase to all the staff, we can update the PAY column by:

UPDATE LECTURERS
SET PAY = PAY * 1.05 ;

6 rows updated.

The PAY column value for all the lecturers will be multiplied by 1.05 (or in other words, a
5% pay rise).

Queries and subqueries can also be used with the UPDATE command just as they can
with the INSERT. This enables you to define complex criteria for choosing exactly the
rows that you want to be modified. As an example, consider this situation. As a result of
human error, all the exam papers for subjects offered by the Engineering department
have been marked down by 4 percent. To correct this in the EXAMS table:

UPDATE EXAMS
SET MARK = MARK + 4
WHERE SUB_NO = ANY
(SELECT SUB_NO
FROM SUBJECTS

2 rows updated.

The query part of this UPDATE finds all the subjects which have a value of 1 in the
DEPT_NO field. As this is a primary key field which refers to the DEPARTMENTS table,
this will only apply to one department, the Engineering department. The subjects offered
by this department are Mathematics, Engineering Drwg., and Electronics and are
returned by the query. The UPDATE adds 4 percent to all exams in these subjects.

The query part of the previous UPDATE requires that you know the value of the
DEPT_NO column for the Engineering department. In most real life cases, you will not
readily have such information at hand. This means that you will either have to run a
separate query on the DEPARTMENTS table to get the value or alternatively, you can
compose an UPDATE command with an additional sub-query:

UPDATE EXAMS
SET MARK = MARK + 4
WHERE SUB_NO = ANY
(SELECT SUB_NO
FROM SUBJECTS
WHERE DEPT_NO =
(SELECT DEPT_NO
FROM DEPARTMENTS

WHERE DEPT_NAME = 'Engineering') ) ;

2 rows updated.

At first, you may have been puzzled at the sequence of chapters in this book. We
started by creating tables, then went straight on to discuss how to query the (already
populated) tables. We described how to populate and update the tables after the
section on querying tables because in order to fully understand the SQL update
commands, you need a firm grasp of composing SQL queries. Trying to make sense of
the last UPDATE command without

5.4 "Removing Rows Form Tables":
The DELETE command.

Sooner or later you will want to delete some of the data from your tables. This might be
incorrect information or redundant data. SQL allows you to remove data by using the
DELETE statement.

DELETE allows you to remove one or several rows from tables. This command
operates on entire rows. It does not allow you to remove individual field values. You
must remove an entire row or not at all.

When used without a predicate, DELETE removes all the rows from a table. To clear
the ELITE_EXAMS table of all data:

DELETE FROM ELITE_EXAMS ;

2 rows deleted.

As with all the SQL update commands, before you can delete from a table, you must
either be the table's owner or you must have been given the necessary privileges by the
owner.

Usually, you do not want to delete all the rows from the table. DELETE allows the use of
the WHERE clause to selectively remove rows from a table. In the STUDENTS table,
suppose that Wendy Wickes decided to leave the course, and we wanted to remove
her record from the table. We can do this by:

DELETE FROM STUDENTS
WHERE SURNAME = 'Wickes' AND FIRST_NAME = 'Wendy Y Y W' ;

1 row deleted.

Although this command does indeed delete the required row from the table, it is not the
best method. If there had been another student with the same name, than that student's
record would also have been removed with this command. In real life situations, where
each table might contain thousands or even hundreds of thousands of rows, we must
be absolutely sure that only the row that we want to be removed is deleted.

It is good policy to first look at the row that is to be deleted by using a SELECT query,
with the same WHERE clause as the intended DELETE statement. To make absolutely
sure that only the right row is deleted, it by referenced only by the primary key field in
the DELETE statement. Thus to remove Wendy Wickes' record:

DELETE FROM STUDENTS

1 row deleted.

This is a foolproof method of removing only the intended row from our table. As the
STUDENT_NO field is a primary key, it is unique for each row and only Wendy Y Y W
Wickes has a value of 16 for this column.

Chapter 6 DATA INTEGRITY

This section looks at the concepts used by SQL to restrict the information that can be
added to the database. Restrictions are usually thought of as negative (constraints,
limitations, confines etc). When they are applied to data integrity, they do a positive job.
ie. that of ensuring you do not inadvertently add junk data to the database. Data
integrity restrictions in effect, act as policemen for the database. They are responsible
for protecting the overall integrity of the database from rogue data that may be
introduced by INSERT and UPDATE statements.

6.1 "Keeping the Data Tidy":
The Basics of Data Integrity.

By definition, a relational database is made up of interrelated tables. The relationships
between each table being formed by foreign and primary keys. Data integrity is
concerned with ensuring that any new data that is added to the tables is compatible with
the existing inter-table relationships. Data integrity is implemented by applying certain
restrictions to the data that is added to and updated in a table. These restrictions can be
broadly divided into four categories; Non-NULL columns, data validity, table integrity
and referential integrity.

6.2 "Fields That Must Have Values":
Non-NULL Columns.

This type of integrity constraint is the easiest to implement and comply with. It is applied
to columns that must have valid values for all rows in the table. These are usually the
primary keys which are used to uniquely identify each row and so must have different
values for each table row. Non-NULL columns are supported by the ANSI/ISO standard
and are implemented by use of the NOT NULL column modifier.

A column must be declared as NOT NULL when the table is first created, in the
CREATE TABLE statement. Subsequent INSERT statements that add rows to the table
are checked by the DBMS to make sure that a value is supplied for the non-NULL
declared column. This check also applies to UPDATE statements where the DBMS
ensures that the proposed update supplies a value for the non-NULL column.

Columns specified as NOT NULL are exactly that. They must contain a value that is not
NULL for all rows in the table. This means that you can supply a value of zero for
numeric type columns or spaces for character type columns. In ensuring this type of
integrity constraint, the DBMS does not check and does not care if the value supplied is
total nonsense. For example, the SURNAME column in the STUDENTS table is defined

as NOT NULL. But the DBMS will still allow you to insert a row into STUDENTS even if
you specify a SURNAME value of '123QRTY456'. Obviously the surname doesn't make
sense, but as it is a non-NULL value, the DBMS accepts it.

6.3 "Values Must be the Right Values":
Data Validity.

This type of data integrity constraint addresses the problem that we touched on at the
end of the last section. It ensures that the right values are inserted into the columns.

The ANSI/ISO standard provides only limited support for confirming data validity. The
DBMS only guarantees that any data added to a column is of the same type as the
column. Recall that the data type of each column must be specified in the CREATE
TABLE statement. This means that if you try to add a text string to a numeric type
column, or vice versa, then the DBMS will reject the operation.

Data type checking still does not ensure the full validity of the data. We could still add
the '123QRTY456' value to the SURNAME column of the STUDENTS table for
example. The fact that the value is enclosed in single quotes tells SQL that it is a
character string. As far as the DBMS is concerned, it is a legal value for the SURNAME
field which was declared as CHAR(15). What we really need to ensure against such
errors is a method of defining a range or a set of valid values for each column. Although
this is not supported by the current ANSI/ISO standard, many commercial SQL systems
vendors provide ways of checking the values that are added to the table. Oracle, for
example has data validity checking built into its data entry forms package. This is a
separate program which checks the data values as they are entered on a form on the
screen. The data values are thus validated before they are submitted to SQL. DB2 also
leaves the data validation to separate programs. It allows you to create external
programs called validation procedures and assign them to each table. DB2 passes the
proposed INSERT and UPDATE column values to the validation procedure which
checks it against its defined parameters. Although validation procedures mean that DB2
does not have to extend the SQL language to support validity checking, they have to be
created by someone with programming experience.

6.4 "Primary key values must be unique":
Entity Integrity.

A primary key in a table has the job of uniquely identifying each row in the table. It is a
bit like the social security number that is allocated to you by the state. It uniquely
identifies you as an individual. Just as there would be serious problems if more than
one person was allocated the same social security number, so it is with primary keys. If
more than one row in the table had the same value for the primary key, then the DBMS

would not be able to distinguish between the rows and the overall integrity of the table
would be lost.

The requirement that primary keys must have a different value for each row is one of
the constraints designed to maintain data integrity. In database jargon, a table is also
known as an entity (the columns are called the attributes of this entity) and this
constraint is called the entity integrity constraint.

The ANSI/ISO standard supports entity integrity by use of the PRIMARY KEY modifier.
Primary keys are defined in the CREATE TABLE statement. The DBMS ensures that all
INSERT and UPDATE statements that affect the primary key do not duplicate values
that are already in the database.

SQL Tips

Formal support for primary keys was added to IBM's DB2 in 1988.

6.5 "All Child Rows must have parents":
Referential Integrity.

Figure 6.1 illustrates how primary, foreign and parent keys are used to relate tables in a
database. The DEPT_NO field in the LECTURERS table is a foreign key which
references a primary key of the same name in the DEPARTMENTS table. This fact by
itself does not tell us much. The underlying concept, the reason for this linkage
however, does. If we look solely at the LECTURERS table, then we can see that R A
Jones is on seniority grade E and earns 24,000. We can also see that he works in
department number 3. Grade E and 24,000 gave us solid information but what does
department number 3 mean? Well, by itself, not much. However, if we know that
DEPT_NO is a foreign key which references the DEPARTMENTS table, then we could
instruct the DBMS to look up the row in the DEPARTMENTS table corresponding to
department number 3. Once we've done that, we could than say that Jones works in the
Management Studies department which has a budget of 2,510,000 etc. The point of this
is that what's important is not so much the relationship itself, but the fact that the
relationship links rows of information in separate tables together. Thus if entity integrity
was not maintained, for example, then there would be two or more departments in the
DEPARTMENTS table which have a DEPT_NO value of 3. We would not be able to
say which one Jones worked in.

The fact that every value of DEPT_NO in the LECTURERS table must have one (and
only one) matching value in the DEPARTMENTS table is known as the referential

integrity constraint. The relationship itself is sometimes called the parent/child
relationship. Each value of DEPT_NO in DEPARTMENTS is a parent. The matching
value(s) in LECTURERS are the child values. Child values must always have only one
parent but parent values can have many children. Lecturers can only work in one
department at a time but a department can have more than one lecturer working in it. In
our examples, the parent and the child columns have the same names but this is not a
requirement.

Referential integrity constraints are concerned with checking INSERT and UPDATE
operations that affect the parent child relationships. For example, the DBMS must make
sure that any row added to the LECTURERS table must supply a value for the
DEPT_NO field which corresponds to an existing value in the DEPT_NO field of the
DEPARTMENTS table. This also applies to updates of the DEPT_NO field in
LECTURERS. Failure to enforce this constraint will result in orphan rows, where a child
value in the LECTURERS table is left with no corresponding parent value in the
DEPARTMENTS table ie. there will be lecturers who will be assigned as working for
non-existent departments.


------- --------- ---- ------ --------
1 Engineering 59 5780000 6200000
2 Arts & Humanities 23 753000 643000
6 Medicine 67 6895000 6932000
/|
|
|
|DEPT_NO in the DEPARTMENTS
|table is a primary key.
|(parent)
|
|
-------------------------
|
|
|
|
|DEPT_NO in the LECTURERS table
|is a foreign key.
|(child)
THE LECTURERS TABLE |
|
------- ----- ------- ------- ----- ----- --- ------
Jones R A 1 1 2 E 24000 03-25-1990
Scrivens T R 2 3 1 D 31800 09-30-1986
Nizamuddin W M 3 3 4 A 86790 05-26-1969
Campbell J G 4 5 3 C 43570 02-23-1980
Ramanujan S 5 4 5 C 40900 01-01-1985
Finley G Y 6 4 5 D 34210 03-28-1960

The foreign key references a parent key which is in almost all cases, a
primary key of the referenced table but it needn't be.

Figure 6.1

SQL Tips

IBM's DB2 added support for referential integrity rules in 1989.

We can also look at this from another point of view. If we deleted or changed rows in
the DEPARTMENTS table, then the DBMS must ensure that the parent key value in
DEPT_NO does not have any child rows left in other tables. In this example we have
only looked at child rows in one table, the LECTURERS table but it is possible for a
parent to have child rows in many tables. Failure to enforce this rule will also create
orphan rows.

Before SQL can enforce referential integrity, it must be told about the inter-relationships
that exist between tables. This is
done when the tables are created. For example, to define the link between the
DEPARTMENTS table and the LECTURERS table, we would specify:

CREATE TABLE DEPARTMENTS (DEPT_NO INTEGER NOT NULL PRIMARY KEY,
DEPT_NAME CHAR(20),
HEAD INTEGER,
BUDGET DECIMAL(10),
P_BUDGET DECIMAL(10)) ;

Table DEPARTMENTS successfully created.

when creating the DEPARTMENTS table and:

CREATE TABLE LECTURERS (SURNAME CHAR(15) NOT NULL,
INITL CHAR(4),
LECT_NO INTEGER NOT NULL PRIMARY KEY,
DEPT_NO INTEGER,
SUB_NO INTEGER,
GRADE CHAR(1),
PAY DECIMAL(6),
JOINED DATE,
FOREIGN KEY(DEPT_NO) REFERENCES DEPARTMENTS);


when creating the LECTURERS table. We do not have to tell SQL which key in
DEPARTMENT is being referenced by the foreign key because it is assumed that it will
be the primary key and each table can only have one primary key. If the foreign key
references a parent key in another table which is not the primary key, then the parent
must be specified. For example, FOREIGN KEY (ROOM_NO) REFERENCES
LECTURERS(OFFICE_NO) where the ROOM_NO foreign key references the

OFFICE_NO parent key.

Some SQL dialects (eg. DB2) allow you to tell the DBMS about rules governing
deletions of rows in the parent table. If you want to delete or change parent key values
that have associated child rows, then they give you one of three options:

1. You can prohibit the deletion from taking place. This is known as the restrict rule and
must be specified in the CREATE TABLE statement of the child table. For example, to
apply the restrict rule to the DEPARTMENTS table:

INITL CHAR(4),
DEPT_NO INTEGER,
SUB_NO INTEGER,
GRADE CHAR(1),
PAY DECIMAL(6),
JOINED DATE,
FOREIGN KEY(DEPT_NO) REFERENCES DEPARTMENTS
DELETE OF DEPARTMENTS RESTRICTED) ;


If you try to delete a row from the DEPARTMENTS table which has associated child
rows in the LECTURERS table, then SQL will reject the command.

2. You can tell the DBMS to apply the changes made to the parent key to the child rows
as well. This is known as the cascade rule and it applies only to the UPDATE
command. The cascade rule is compatible with the restrict rule. So for example, you
can tell the DBMS to reject deletions of parent keys but allow alterations and pass any
changes on to the child rows:

INITL CHAR(4),
DEPT_NO INTEGER,
SUB_NO INTEGER,
GRADE CHAR(1),
PAY DECIMAL(6),
JOINED DATE,
DELETE OF DEPARTMENTS RESTRICTED
UPDATE OF DEPARTMENTS CASCADES) ;


If you now UPDATE the value of the Physical Sciences row in the DEPARTMENTS

table and set DEPT_NO to 9, then the cascade rule will also update the row of
Campbell in the LECTURERS table and set DEPT_NO to 9 as well. Note that if we
specified only the cascade rule, then SQL would have allowed deletions of the parent
key and it would have cascaded the deletion down to the child rows as well ie. it would
have deleted the associated child rows.

3. You can tell the DBMS to allow updates to the parent keys but set the value of the
foreign keys to NULL. This rule is known as the set to NULL rule. For example to tell the
DBMS to implement the set to NULL rule on the DEPARTMENTS table:

INITL CHAR(4),
DEPT_NO INTEGER,
SUB_NO INTEGER,
GRADE CHAR(1),
PAY DECIMAL(6),
JOINED DATE,
DELETE OF DEPARTMENTS NULLS) ;


For example, if the Physical Sciences department is to be axed, SQL will allow us to
delete it's row from the DEPARTMENTS table, but will set the value of DEPT_NO for
Campbell's row in the LECTURERS table to NULL. Note that the set to NULL rule will
only work on columns that do not have the NOT NULL constraint. It is safer than the
"cascade the delete" rule in that child rows are not actually deleted from the tables.

6.6 "Integrity Requirements of the User":
SQL Triggers.
The integrity constraints that we have seen so far have all been implemented to ensure
the validity of the overall data. In a typical organization, there are other rules that apply
to normal day to day transactions. These will also need to be reflected in the database,
but their non-enforcement will not invalidate the data integrity. For example, say it is
university policy that when a new value is added to the BUDGET column in the
DEPARTMENTS table, then the old BUDGET value must be written to the P_BUDGET
field. The DBMS will not be bothered too much if this rule is not enforced because it
does not affect any important database relationships. These types of constraints are
non-critical, and the SQL language does not support them directly.

To address these integrity requirements (which are important from the user's point of
view), some commercial SQL implementations have added what are known as SQL
triggers to their functionality. Triggers were first implemented by Sybase in 1986. The
concept of triggers is similar to the DB2 concept of validation procedures. You can
define a set of operations, collectively called a trigger, that the DBMS must execute

whenever there is a change in the contents of a table. For example, to enforce the rule
on alterations to the BUDGET column using Sybase triggers, the command to create
the trigger would be:

CREATE TRIGGER UPDATE_BUDGET
ON DEPARTMENTS
FOR UPDATE
AS UPDATE DEPARTMENTS
SET P_BUDGET = BUDGET
FROM DEPARTMENTS, INSERTED
WHERE DEPARTMENTS.DEPT_NO = INSERTED.DEPT_NO

This command creates a trigger called UPDATE_BUDGET and tells the DBMS to
activate it whenever a row is updated in the DEPARTMENTS table. The trigger itself
sets the value of P_BUDGET to the current value of BUDGET for the row where the
DEPT_NO value matches the DEPT_NO for the update row (Note the new row values
are identified by the INSERTED prefix).

As with DB2's validation procedures, the complexity of triggers means that they usually
require a programmer to set them up. Triggers also add a lot of hidden logic to
database operations. Seemingly simple SQL commands may have hidden triggers
associated with them which may require a lot of additional processing. To a certain
extent, this takes away some control you have over the database.

Chapter 7. VIEWS

We've created them, dropped them and used them quite a lot. By now you have a
pretty good idea what a database table is and how you can use it store and retrieve
information. In this chapter, we will introduce you to another database object (remember
that a table is a database object) called the view. Whereas the rows in a table are
based on the contents of a physical disk file, the contents of a view are derived from the
rows of other tables. In this respect, views are similar to queries in that they too derive a
results table based on the contents of other tables. This similarity is more than just
coincidence. Views are in fact defined by an SQL query and their contents are the
results of executing that query. The difference between queries and views is that views
can be queried just like tables. The query defining the view is run (to derive the contents
of the view) every time the view becomes the subject of a query.

Figure 7.1 shows the relationship between tables and views. In the rest of this chapter,
we will be using the term base tables to refer to the actual SQL tables that you have
been using so far and virtual tables to refer to views.

7.1 "Restrict the Data You Can See":
What is a view?

Most textbooks on the subject like to describe views as windows into base tables. The
argument being that views let you look at the table as though through a window.
Conceptually, this doesn't make sense. The purpose of a window is to look through it
and see the outside world in all it's detail. So following on from that, is the idea behind
views to look through them and see the contents of the base tables in detail? Not at all.
The main point of views is to restrict what you can see of the underlying base tables.
The use of views is similar to the use of blinkers. Blinkers are leather sidepieces
attached to a horse's bridle which prevent the horse being distracted by side-vision. In
the same way, views are applied to tables to prevent users from having access to all the
data in the tables. There are four good reasons why we might need to do this.

1. Database security. Views allow you to restrict user's access to only those sections of
the database that directly concerns them. In the LECTURERS table for example, you
will not want every casual user to be able to access the lecturer's salary information.

2. Data integrity. The DBMS can check the data entered through views to ensure that it
meets the conditions defined in the view query.

3. Shielding from change. The view presents a uniform front to the user even if the
underlying base table structure is modified, the view can remain constant.

4. Easier querying. Because the view is itself a result of a query, it can reduce complex
multi-table queries down to simple SELECT statements.

A view cannot be based on more than one query. This means that you are not allowed
to use the UNION clause in the view definition (CREATE VIEW) statement.

7.2 "How To Make Views":
The CREATE VIEW command.

Views are created by the CREATE VIEW command. The syntax of this command is
shown in Figure 7.2. You can specify the names of the columns for the view if you wish,
but it is optional. For example, to restrict users' view of the LECTURERS table to the
first five columns only, you could create a view as:

CREATE VIEW TEACHING_STAFF
AS SELECT SURNAME, INITL, LECT_NO, DEPT_NO, SUB_NO
FROM LECTURERS ;
FROM TEACHING_STAFF ;

View TEACHING_STAFF successfully created.

CREATE VIEW view_name [ READ_ONLY ]
( col_name {, col_name }* )
AS select_statement [ WITH CHECK OPTION ] ;

view_name
The name of the view.

col_name
The name of the view's columns. These are optional.

select_statement
The SELECT statement which is used to create the view.

Figure 7.2

The view is called TEACHING_STAFF and is made up of the first five columns from the
LECTURERS table. The names of the columns in the view will be the same as the
corresponding columns in the base tables. If as the database administrator, we deny
casual users access to the LECTURERS table directly and let them use the view
TEACHING_STAFF instead, confidential information such as salary, seniority, grade
etc. will be secure. The view can be queried just like any other table:

SELECT *
FROM TEACHING_STAFF ;

SURNAME INITL LECT_NO DEPT_NO SUB_NO
---------------- ----- ------- ------- ------
Jones R A 1 1 2
Scrivens T R 2 3 1
Nizamuddin W M 3 3 4
Campbell J G 4 5 3
Ramanujan S 5 4 5
Finley G Y 6 4 5

To look at the contents of the TEACHING_STAFF view, or:

SELECT *
FROM TEACHING_STAFF
WHERE DEPT_NO = 3 ;

SURNAME INITL LECT_NO DEPT_NO SUB_NO
--------------- ----- ------- ------- ------
Scrivens T R 2 3 1
Nizamuddin W M 3 3 4

To selectively retrieve rows from the view.

The views we have described so far, allow access to only certain columns in the tables,
are known as vertical views. You can think of them as being made up of vertical slices
(columns) of the base table.

Horizontal views as you might have guessed consist of horizontal slices (rows) of data
in the table. For example, this command creates a horizontal view which limits access
of the STUDENTS table to the rows of first year students only:

CREATE VIEW FRESHMEN
AS SELECT *
FROM STUDENTS
WHERE YEAR = 1 ;

View FRESHMEN successfully created.

The concept of horizontal and vertical views is not absolute in any way. Indeed, most
views are a combination of both. For example, this view, which restricts access to
certain columns of first year students in the STUDENTS table is both a horizontal and a
vertical view:

CREATE VIEW FRESHMEN_2
AS SELECT SURNAME, FIRST_NAME, DEPT_NO, YEAR
FROM STUDENTS
WHERE YEAR = 1 ;

View FRESHMEN_2 successfully created.

7.3 "Looking Through the Window":
Using Views.

When you query a view, you are in fact performing a query on another query. If we
query the FRESHMEN_2 view and retrieve those students who study in department 5
as this query does for example:

SELECT *
FROM FRESHMEN_2
WHERE DEPT_NO = 5 ;

SURNAME FIRST_NAME DEPT_NO YEAR
--------------- --------------- ------- ----
Layton Hugh 5 1

then we are in fact looking at those students who are in department 5 and who are also
freshmen ie. whose YEAR column has a value of 1. The query is in effect a combination
of the query used to create the view and the query and the query operating on the view.
Thus the query on the view can also be expressed in terms of the base table as:

SELECT SURNAME, FIRST_NAME, DEPT_NO, YEAR
FROM STUDENTS
WHERE YEAR = 1
AND DEPT_NO = 5 ;

SURNAME FIRST_NAME DEPT_NO YEAR
--------------- --------------- ------- ----
Layton Hugh 5 1

This combination of view query and the query on the view sometimes leads to
problems. For example, this view lists the number of students in each department:

CREATE VIEW STUDENT_NUMBERS(DEPT, STUDENTS)
AS SELECT DEPT_NO, COUNT(*)
FROM STUDENTS
GROUP BY DEPT_NO ;

View STUDENT_NUMBERS successfully created.

A query on this view to find how many departments have more than 5 students, such
as:

SELECT *
FROM STUDENT_NUMBERS
WHERE STUDENTS > 5 ;


is rejected by SQL. The reason for this is that the combination of the two queries results
in an illegal query:

SELECT DEPT_NO, COUNT(*)
FROM STUDENTS
WHERE COUNT(*) > 5
AND GROUP BY DEPT_NO ;


The STUDENTS column in the view is in fact a calculated column based on COUNT(*).
SQL does not allow aggregate functions to be used in a predicate so the query is
rejected. If you not aware of the underlying query used to create the view, then it
appears as if SQL has rejected a perfectly legal query.

We listed one of the advantages of using views as leading to easier querying. The
chapter on multi-table querying and joins has shown us how complex some SQL
queries can get. Views allow you to transfer most of the complex structure of such
queries into the view definition itself. Subsequent queries operating on the view will
themselves be simple, but the database activity needed to retrieve the results will still be
the same. Consider this query which lists the exams taken by students along with the
subject and student names. The query implements a three table join:

SELECT A.MARK, B.SUB_NAME, C.SURNAME
FROM EXAMS A, SUBJECTS B, STUDENTS C
WHERE A.SUB_NO = B.SUB_NO
AND A.STUDENT_NO = C.STUDENT_NO ;

A.MARK B.SUB_NAME C.SURNAME
------ -------------------- ------------
76 Mathematics Duke
42 Electronics Duke
67 Engineering Drwg Duke
52 English Lit Al-Essawy
89 English Lit Ayton
34 Basic Accounts Patel
49 Marketing Patel
62 Industrial Law Jones
70 Industrial Law Scott
36 Industrial Law Baker
52 Industrial Law Brown
67 Organic Chemistry Monkhouse
82 Organic Chemistry Grimm
73 Organic Chemistry Gyver
27 Physiology Hung-Sun
56 Anatomy Hung-Sun
67 Anatomy Middleton
63 Physiology Middleton

Additional predicate clauses need to be applied to this basic query to selectively retrieve
exam records. Such a query is an ideal candidate for a view. To create a view that joins
the EXAMS, the SUBJECTS and the STUDENTS table:

CREATE VIEW EXAM_MARKS(MARK, SUBJECT, STUDENT)
AS SELECT A.MARK, B.SUB_NAME, C.SURNAME
FROM EXAMS A, SUBJECTS B, STUDENTS C
WHERE A.SUB_NO = B.SUB_NO
AND A.STUDENT_NO = C.STUDENT_NO ;

View EXAM_MARKS successfully created.

It is now a simple matter to query this view. For example, to look at the rows of those
students who score more that 70% in their exams, we only have to run this simple
query on the EXAM_MARKS view:

SELECT *
FROM EXAM_MARKS
WHERE MARK > 70 ;

MARK SUBJECT STUDENT
----- -------- ---------
76 Mathematics Duke
82 Organic Chemistry Grimm
73 Organic Chemistry Gyver

You are not limited to preforming simple queries on this view. SQL lets you join views to
base tables and also to other views, so to look at the marks scored by students from
department number 4, we will need to join the EXAM_MARKS view with the
STUDENTS base table:

SELECT A.SURNAME, A.MARK, B.DEPT_NO
FROM EXAM_MARKS A, STUDENTS B
WHERE B.DEPT_NO = 4
AND A.STUDENT = B.SURNAME ;

A.SURNAME A.MARK B.DEPT_NO
------------ ------ ---------
Duke 76 4
Duke 42 4
Duke 67 4
Al-Essawy 52 4
Baker 36 4
Gyver 73 4

You can also create and use views with subqueries and use views to create other
views. This query is an example of a both. The ABOVE_AVERAGE view lists the rows
from the EXAM_MARKS view where a student's mark is included only if it is greater
than the average mark for all exams:

CREATE VIEW ABOVE_AVERAGE
AS SELECT MARK, STUDENT, SUBJECT
FROM EXAM_MARKS A
WHERE A.MARK >
(SELECT AVG(B.MARK)
FROM EXAM_MARKS B) ;

View ABOVE_AVERAGE successfully created.

Overall, the ABOVE_AVERAGE view is a result of a lot of DBMS activity but it can be
queried just as any other table. Even if we didn't know exactly what it did, we could tell
by the name that ABOVE_AVERAGE is going to be a list of values which are greater
than the average value of something. This case highlights another important point that
you should keep in mind. You should give your queries meaningful names so that it is
obvious what the view does. We can tell for example that the ABOVE_AVERAGE view
lists the exams where the mark is above the average mark. If we had named it say,
EXAMS_VIEW24, then we would need to refer to the CREATE VIEW statement to find
out exactly what the view is doing.

7.4 "Changing Data Through Views":
Updating Views.

Updating (remember that this term includes the INSERT, UPDATE and the DELETE
statements) data through views can present some problems. Unlike base tables, views
allow you to specify aggregate functions as part of the CREATE VIEW definition. By
definition, the data in these columns is derived from calculations performed in the base
table rows and you will not be able to update data in these columns through the DML
commands.

The ANSI/ISO standard specifies that for a view to be updatable, the rows and columns
in the view must be directly traceable to the base table that comprise the view. This
means that the CREATE VIEW statement must:

-Specify only one base table.

-Not include aggregate functions in the column definitions.

-Not use GROUP BY or HAVING.

-Not use DISTINCT to eliminate duplicate rows from the view.

-Select only simple columns. ie. expressions, string constants etc. cannot be used.

-Include all the columns from the base table that are defined as NOT NULL.

So the view TEACHING_STAFF is updatable because all it's rows relate directly to
those of the LECTURERS base table. ie:

UPDATE TEACHING_STAFF
SET SUB_NO = 9
WHERE LECT_NO = 4 ;

1 row updated.

will change the value of SUB_NO 9 for lecturer number 4. The effect will be the same
as performing the UPDATE on the LECTURERS base table itself.

The STUDENT_NUMBERS view on the other hand is not updatable because its
definition contains the aggregate function COUNT(*).

7.5 "Verifying Data Changes":
The WITH CHECK Option.

Even though a view is updatable, this does not mean that all updates will be trouble
free. One particular problem occurs when you add data to through the view. When a
row is added to an updatable view, sometimes, it appears as if the row that you added
has gone down a black hole. It is never seen in the view again. Acute observers of SQL
queries may already have guessed how this can come about. In order to explain it, let's
consider an example. Consider the view:

CREATE VIEW LOW_CREDITS
AS SELECT SUB_NO, SUB_NAME, CREDITS
FROM SUBJECTS
WHERE CREDITS = 1 ;

View LOW_CREDITS successfully created.

It limits access of the SUBJECTS table to only those subjects where the value for
CREDITS is 1. If we now add a row through the view:

INSERT INTO LOW_CREDITS
VALUES (11, 'Geology', 2) ;

1 row inserted.

The INSERT operation will succeed, but the row won't be visible through the view:

SELECT *
FROM LOW_CREDITS ;

Mo matching records found.

The reason for this is that in the predicate of the CREATE VIEW statement, we
specified CREDITS = 1. The value for the CREDITS was 2 in the INSERT. Thus this
row will not be accessible through the view even though the view has been entered into
the base table. You will not be able to query, update or delete the row via this view.

In order to overcome this problem, SQL allows you to use the WITH CHECK OPTION
clause in the view definition statement. For example, if we had created the
LOW_CREDITS view with this option:

CREATE VIEW LOW_CREDITS
AS SELECT SUB_NO, SUB_NAME, CREDITS
FROM SUBJECTS
WHERE CREDITS = 1
WITH CHECK OPTION ;

View LOW_CREDITS successfully created.

SQL will now check every INSERT and UPDATE statement that operates on this view
against the predicate of the view. If the values in the proposed INSERT conflicts with
the predicate of the view, then the command will be rejected. It is always a good idea to
use the WITH CHECK OPTION clause in the view definition statement of updatable
views as it eliminates the chance of typing errors etc. from adding rows to the base
table which the user will not be able to delete even if he realises the mistake.

7.6 "Shutting the Window":
The DROP VIEW Command.

Views can be deleted from the database by the DROP VIEW command. For example to
delete the LOW_CREDITS view:

DROP VIEW LOW_CREDITS ;

View LOW_CREDITS successfully dropped.

The view will be removed from the database and all queries that reference it will fail.
Removal of a view does not affect the underlying base tables or any of the records in
them.

Chapter 8 DATABASE SECURITY

Most SQL based systems operate in a multi-user environment. This means that at any
time, several different users can access the same database to query, insert, update or
delete data. Such an environment requires safety devices that are both built into the
DBMS itself and that prevent users from inadvertently corrupting the data. This chapter
looks at the security features that are built into SQL itself and also addresses some of
the wider aspects of database security.

The DBMS must implement security on two levels. First at the overall database level
and second at the individual record level. In this chapter we will be dealing with
database security designed to prevent unauthorized access at the overall database
level.

8.1 "The Term Security is Defined as Protection":
SQL Privileges.

The term security is defined as protection, defense and safety. In this context, it is a
very important aspect of an SQL (or any other) DBMS. Specifically, as part of it's
security features, the DMBS should protect the data from unauthorized access. It
should only allow approved users to use the data in the database and even then, only
allow them to perform those functions for which they have authorization.

Without any security features, the data in the database will be accessible by all users.
Anyone who felt like it could alter the rows in the tables either inadvertently or
maliciously. In almost all organizations, this is not an acceptable state of affairs for a
database that might hold vital business and personnel data to be in. Fortunately, the
SQL language implements database security as an integral part of it's DDL structure.

SQL security is based on the concept of privileges. A privilege can be thought of as
permission to perform a certain operation on a certain database object given to a
certain user. There are three important concepts here. The first is the privilege which is
what we have just described. The second is the idea of database operations which are
the actions that you may want to restrict for certain users. Essentially, these operations
boil down to queries, insertions, updates and deletions of data. The third is the idea of
users who are the people who use the database system and issue SQL commands.
The DBMS needs to be aware of everyone who is using the database at any time.

The ANSI/ISO standard defines four privileges that can be granted to or revoked from
users: SELECT, INSERT, UPDATE and DELETE. These privileges correspond to the
SQL operations that a user is allowed to perform on a given table. The SELECT

privilege allows the user to query a table or view. The INSERT privilege allows the user
to add rows to a table or view. The DELETE privilege allows the user to delete rows
from a table or view and the UPDATE privilege allows the user to modify data in a table
or view. Unlike the other three privileges, you can grant UPDATE on selective columns
in a table or a view.

8.2 "Users Must Introduce Themselves":
The Logon Procedure.

In order to be able to implement system wide security, the DBMS needs to be aware of
exactly who is using the database at any point in time. To do this, almost all commercial
SQL DBMSs rely on the concept of authorization-ids. The authorization-id is a label by
which SQL identifies each person who is allowed to issue commands to the DBMS
(note that a group of users may have similar requirements and may thus share an
authorization-id, but is not very common. Usually, each user has his or her own
authorization-id). An authorization-id may also be used to identify a program rather than
a person that issues SQL commands.

SQL Tips

SQL Server and Sybase support group-ids which can be used to identify groups of
users with similar needs.

New users are registered onto the system by the database administrator who must tell
the DBMS to add the new user's authorization-id and password to the list of valid users.

Some commercial SQL implementations, including Ingres and Informix, use the
username that is specified in the host computer's logon procedure as the authorization-
id for the user. Other systems including Oracle require users to specify the username
and also an associated password at the start of the interactive SQL session. The
username is used as the authorization-id, but the password is not used in SQL.

SQL Tips

The ANSI/ISO standard uses the term authorization-id instead of user-id.

8.3 "The Library Database":
An example system.

For any table that you create, SQL assigns you as the owner of that table. Ownership of
tables means that you automatically have full privileges for that table (these are the four
standard ANSI/ISO privileges; SELECT, INSERT, UPDATE and DELETE, already
described as well as any other non-ANSI privileges that are supported by your particular
dialect of SQL). Initially, all the other users of the database will have no privileges on
your new table. Figure 8.1 shows a group of users and the lending library database that
they use. The structure of this simple database will be explained as we progress
through this chapter.

The users:
FRANK BARBARA ADAM PUBLIC JONES MORRIS
O O O ☺☺☺☺☺☺ O O
-|- -|- -|- ☺☺☺☺ -|- -|-
I I / I I ☺☺ I I I I
I I I~I I I ☺☺ I I I I
/ /
| is owned by |
------------------ |
| |
| |is owned
| |by
| |
The tables: | |
------------------ | ----------
|__|_|___|_|__|__|_| -- |__|____|__|
|__|_|___|_|__|__|_| |__|____|__|
|__|_|___|_|__|__|_| |__|____|__|
|__|_|___|_|__|__|_| |__|____|__|
|__|_|___|_|__|__|_| |__|____|__|
|__|_|___|_|__|__|_| |__|____|__|

------------------ ----------
BOOKS /| VIDEOS
|is based
--- on
The views: |
~~~~~~~~~
|_______|_|
|_______|_| (Point)
|_______|_| --------> TO BARBARA
|_______|_|
|_______|_|
TITLES

THE CONTENTS OF VIDEOS:
('Star Trek','Universal', 'PG')
('Duck Tales','Disney', 'U')

THE CONTENTS OF BOOKS:
('English','U.K.Author', 'IN')
('French','E.E.C.Author', 'OUT')

THE CONTENTS OF TITLES view:
('German', 'Otto Matic');

Figure 8.1

SQL Tips

The ANSI/ISO standard allows authorization-ids to be up to 18 characters long, but
many commercial implementations do not stick to this.

User Frank logs onto the system under the authorization-id FRANK and creates a table
called BOOKS by:

CREATE TABLE BOOKS (
TITLE CHAR(10),
AUTHOR CHAR(15),
STATUS CHAR(3)) ;

Table BOOKS successfully created.

FRANK is now the owner of the BOOKS table. No other user is allowed to access the
table. If FRANK adds two rows of data to his table, then SQL accepts this because
FRANK has the INSERT privilege on the BOOKS table:

INSERT INTO books
VALUES ('English','U.K.Author', 'IN');

INSERT INTO books
VALUES ('French','E.E.C.Author', 'OUT');


But if user ADAM tries to add a row, then SQL will reject his command because he
does not have the necessary privilege:

INSERT INTO BOOKS
VALUES ('Spanish','E.S.Panya', 'OUT') ;

Error 136: User does not have INSERT
privileges.

Similarly, the VIDEOS table is owned by PUBLIC (this is a special authorization
identifier which means that all the users have ownership rights on it), and the view
TITLES is owned by BARBARA. Notice that BARBARA doesn't own the BOOKS table
on which the view is based.

SQL only lets you create a view, if you have the SELECT privilege on every table used
in the view. Ownership of the view will thus guarantee you only the SELECT privilege
on it. The other privileges will only be given to you if you already have them for all the
base tables used in the view. Thus if you have the INSERT privilege on all of the tables
used in the view, then you will also get the INSERT privilege on the view. In the lending
library database, user BARBARA must have been granted at least the SELECT and the
INSERT privileges on the BOOKS base table in order to be able to create the view and
add a row to it.

8.4 "How privileges are passed":
The GRANT and REVOKE commands.

In all the SQL commands used in this book so far, it was assumed that the user was
referring to tables that he either owned or ones for which he had been granted the
required privileges by the owner of the table. In live database systems, very few users
actually own the tables that they query.

SQL Tips

The ANSI/ISO standard specifies only four privileges for tables and views:
SELECT, INSERT, UPDATE and DELETE.

Database users are given access to tables, views (collectively known as database
objects) and columns by the GRANT statement. This is part of SQL's DDL (data
definition language) and is a part of the ANSI/ISO standard. The opposite of GRANT is
REVOKE. Privileges that were granted with GRANT are rescinded by the REVOKE
command. REVOKE is not included in the ANSI/ISO standard, but is so widely used
that it has become almost a de facto standard. The syntax of the GRANT and REVOKE
statements is shown in Figures 8.2 and 8.3 respectively.

GRANT rights ON tbl_name TO auth_id ;
[ WITH GRANT OPTION ]

rights
The rights GRANTed may be:
ALL PRIVILEGES |
SELECT | INSERT | UPDATE | DELETE
{ , SELECT | INSERT | UPDATE | DELETE }*

tbl_name
The table or view name on which privilege(s) are to be GRANTed.
This may be up to 24 characters.

auth_id
The authorization identifier to which privilege(s) are to be
GRANTed.

Figure 8.2

REVOKE rights ON tbl_name FROM auth_id ;

rights
The rights REVOKEd may be:
ALL PRIVILEGES |
SELECT | INSERT | UPDATE | DELETE
{ , SELECT | INSERT | UPDATE | DELETE }*

tbl_name
The table or view name on which privilege(s) are to be REVOKEd.
This may be up to 24 characters.

auth_id
The authorization identifier from which privilege(s) are to be
REVOKEd.

Figure 8.3

The table's owner must explicitly grant privileges to all other
users who need to use the table. For example, if FRANK wanted to allow BARBARA to
be able to query the BOOKS table, then he must grant her the SELECT privilege on
BOOKS:

GRANT SELECT ON BOOKS TO BARBARA ;

Privileges successfully granted.

8.4.1 Using views to limit access to columns.

The ANSI/ISO standard doesn't let you to specify columns as arguments to the
SELECT privilege command. This means that you must grant SELECT rights for the
whole of the table or for none of it. You can get round this stipulation by defining a view
which only displays the data that you want the user to see and granting him the
SELECT privilege on the view and not on the base table. For example, if BARBARA
grants ADAM the SELECT privilege on the TITLES view by:

GRANT SELECT ON TITLES TO ADAM ;


then ADAM will be able to query the view as much as he likes, but he won't be able to
update the data in the view and he won't have any access to the view's base table. SQL
will thus reject this query from ADAM:

SELECT *
FROM BOOKS;

Error 137: User does not have SELECT
privileges.

A serious drawback with using views solely to implement a security structure is the
considerable processing overhead that they incur. Indiscriminate use of views can
significantly reduce the response time of the overall database.

8.4.2 The ALL PRIVILEGES and PUBLIC keywords.

Grant allows you to bestow more than one privilege in a single statement, but does not
let you specify more than one authorization-id. Thus FRANK can grant all four privileges
to BARBARA on the BOOKS table by:

GRANT SELECT, INSERT, UPDATE, DELETE
ON BOOKS
TO BARBARA ;


Some DBMS have broken from the ANSI/ISO standard and allow you to specify a list of
authorization-ids as well as privileges in the GRANT statement. For example, this
statement is perfectly legal in DB2's SQL dialect:

GRANT SELECT, INSERT, UPDATE, DELETE
ON TO BARBARA, MORRIS ;


If you want to grant all the available privileges to a user, ANSI/ISO SQL allows you use
the ALL PRIVILEGES clause as a shortcut. So if FRANK wants to give all privileges on
the BOOKS table to PUBLIC for example, then he could specify:

GRANT ALL PRIVILEGES ON BOOKS TO PUBLIC ;


The PUBLIC keyword is a special authorization-id that applies to all users. So the above
GRANT statement effectively issues a free for all on the BOOKS table. All users are
allowed to perform all operations on BOOKS. This is not a good idea as it means that
FRANK now has no control over who is allowed to alter or add data to his table. If an
inexperienced user modifies some of the rows without regard to the inter-table
relationships that exist, it could easily result in loss of data integrity

SQL Tips

Oracle and IBM's DB2 and SQL/DS support and ALTER TABLE privilege and a
CREATE INDEX privilege.

8.4.3 Selectively granting the UPDATE privilege.

The SELECT, INSERT and the DELETE privileges, must be either granted for all the
columns in a table (or view) or for none of them. The exception to this is the UPDATE
privilege. The ANSI/ISO standard allows you to grant the UPDATE privilege selectively
for individual columns of a table or view. So if in the lending library example, if user
ADAM is to be allowed to update the STATUS column of the BOOKS table but not the
TITLE or the AUTHOR columns, then FRANK, the owner of the table, can grant this
limited update privilege by:

GRANT UPDATE(STATUS)
ON BOOKS
TO ADAM ;


Now, ADAM will be able to update the status of books as they are lent out or bought in,
but he will not be able to change the values of the TITLE and AUTHOR columns. The
column list in UPDATE is optional. If it is omitted, then SQL assumes that the UPDATE
privilege is to apply to all the columns. For example, this command gives BARBARA
update rights for all columns in the BOOKS table:

GRANT UPDATE
ON BOOKS
TO BARBARA ;


8.4.4 Allowing grantees to grant privileges.

All the privileges have so far been granted by the owners of the relevant tables and
views. But what if a user who himself was granted privileges, a grantee, wants to grant
those privileges to other users. The owner can allow this by specifying a WITH GRANT
OPTION clause in the initial grant statement. For example, Figure 8.4 shows the chain
of privileges that we want to establish. The BOOKS table is owned by FRANK who
wants to grant the SELECT privilege on it to BARBARA. User BARBARA in turn wants
to grant the SELECT privilege to MORRIS. To accomplish this, FRANK must first grant
BARBARA the SELECT privilege:

FRANK BARBARA MORRIS
O O O
-|- -GRANT SELECT TO --> -|- --GRANT SELECT TO --> -|-
I I WITH GRANT OPTION / I I
I I I~I I I
/
| |
| |
| is owned by |
| Now MORRIS can |
| query the BOOKS |
The BOOKS table. table. |
------------------ |
|__|_|___|_|__|__|_| |
|__|_|___|_|__|__|_| <----------------------------------
|__|_|___|_|__|__|_|
|__|_|___|_|__|__|_|
|__|_|___|_|__|__|_|
|__|_|___|_|__|__|_|

Figure 8.4

GRANT SELECT
ON BOOKS
TO BARBARA
WITH GRANT OPTION ;


BARBARA now has the SELECT privilege on BOOKS. As well as this, the WITH
GRANT OPTION lets her grant this privilege to other users as well. Note that the WITH
GRANT OPTION only applies to the privileges and the table (or view) named in the
GRANT statement. So for example, BARBARA will not be able to grant INSERT or
DELETE or UPDATE rights on BOOKS to anyone (how can she, she doesn't have
them herself). However, BARBARA can let MORRIS (or anyone else) have the
SELECT privilege by:

GRANT SELECT
ON BOOKS
TO MORRIS ;


Now, MORRIS has SELECT privileges on BOOKS as well. If BARBARA had specified
WITH GRANT OPTION for MORRIS, it would have allowed MORRIS to grant SELECT
rights on BOOKS as well. The original owner of BOOKS, FRANK will know nothing of

this chain of privileges propagating through the users, and will in effect have lost charge
of the BOOKS table. For tight control of access rights, it is a good idea to be careful
about who gets the WITH CHECK option.

8.5 "Taking back privileges":
The REVOKE statement.

All commercial SQL vendors offer the REVOKE command as a method of taking back
the privileges that were granted with GRANT. The REVOKE command is in fact an
extension to the ANSI/ISO standard. ANSI/ISO SQL includes the GRANT command on
the assumption that a database designer will have finalised the design on paper first
and then use the DDL commands to create all the tables, views and security privileges.
The standard gives the designer a very limited ability to change his mind once the
design has been implemented. So under ANSI, once privileges have been assigned,
they can only be changed with great difficulty.

SQL Tips

The REVOKE statement is totally absent from the ANSI/ISO standard.

The format of REVOKE that's implemented by most commercial SQL systems is shown
in Figure 8.3. It's format is very similar to the GRANT statement. For example, this
command revokes the INSERT and the UPDATE privileges on the TITLES view from
MORRIS:

REVOKE INSERT, UPDATE
ON TITLES
FROM MORRIS ;

Privileges successfully revoked.

You can also use the ALL PRIVILEGES clause in the REVOKE command. For
example, to stop ADAM using the BOOKS table, FRANK could specify:

REVOKE ALL PRIVILEGES
ON BOOKS
FROM ADAM ;


REVOKE only allows you rescind those privileges that you yourself granted. This

means that if we consider the privilege chain discussed previously and shown in Figure
8.4, FRANK cannot revoke the SELECT privilege on the BOOKS table from MORRIS
even though he is the owner of the table. He can however revoke this privilege from
BARBARA by:

REVOKE SELECT
ON BOOKS
FROM BARBARA ;


On most systems this will result in the DBMS automatically revoking the corresponding
privilege from all the grantees lower down the chain. Thus MORRIS will also lose the
SELECT rights on BOOKS as it was granted by BARBARA. As REVOKE is a non-
standard feature, different dialects of SQL implement it in subtly different ways. This
cascading effect of REVOKE may not be a feature of your particular dialect of SQL.

Chapter 9 TRANSACTION PROCESSING
Most database updates are implemented by a series of two, three or more individual
SQL statements. For example, if a lecturer who is also a head of a department leaves
the university, we would first have to UPDATE the DEPARTMENTS table and set the
HEAD field value for the lecturer's department to NULL. Then we would have to delete
the lecturer's record from the LECTURERS table. In SQL, this two statement process is
called a transaction. The job of this particular transaction is to delete a lecturer's details
from the database. Each SQL statement in the transaction performs a part of the overall
task, and all the statements must be executed for the database to be in a consistent
state.

Complex transactions can involve five or six update (remember this includes INSERT,
UPDATE and DELETE) statements and if some of them are executed by the DBMS
and for some reason the others are not, the data integrity of the data will be lost. To
guard against this, SQL provides a way of reversing the effects of commands that
modify data so if a crash does occur in the middle of a transaction, the partially
completed transaction can be discarded and the database data returned to it's initial
state before the transaction was run.

9.1 "A Transaction as a Fundamental Unit of Work":
The COMMIT and ROLLBACK commands.

The DBMS must ensure that a transaction is treated as a fundamental unit of work. This
means that once the DBMS has started processing the first statement in a transaction, it
must carry on until all the remaining statements are also processed, the transaction
cannot be half-processed. All the statements in the transaction must be treated as a
single unit.

If processing is halted in the middle of a transaction, as a result of a re-boot, a system
crash for example, then the DBMS must reverse the state of the database to that which
existed before the transaction was started. The special SQL command to do this is
ROLLBACK. If all the statements in the transaction are successfully executed, then the
database changes are made permanent by the COMMIT command. COMMIT and
ROLLBACK are regular ANSI/ISO standard SQL statements, like SELECT, INSERT
etc., that can be used in both programmatic and interactive SQL. The ANSI/ISO
standard specifies that transaction processing must always be in effect. It starts with the
start of the interactive SQL session or the user program and ends with either a
COMMIT command, a ROLLBACK command, the termination of the user program or a
catastrophic system crash.

9.1.1 A practical example of transaction processing.

You will understand the ideas behind transactions and the need for them by looking at a
practical example. Figure 9.1 shows an invoice processing system. The two tables
represent two sections of an invoice. The INV_HEADER table is the invoice header
which holds the details of invoice number, the customer number who the invoice is
made out to, the date of invoice and the total number of items that have been ordered.
The INV_BODY table holds such details as the items that appear on the invoice. It has
fields for invoice number, the quantity of each item, the description of the item and the
total value of the items. As each order is received, the staff must add a row to the
INV_HEADER table for the new invoice, and rows to the INV_BODY table for the items
ordered. A customer can ask for several different items in a single order, so each row of
INV_HEADER can have several associated rows in the INV_BODY table.

Figure 9.2 shows how a new order is added to the invoicing system. Without any
transaction processing, the rows in INV_HEADER and INV_BODY can get into an
inconsistent state. For example, if SQL succeeds in adding an invoice header row to
INV_HEADER, but fails in inserting all the items into INV_BODY, the ITEMS figure in
INV_HEADER will not equal the sum of the QTY values in INV_BODY. It will be worse
still if we add the items first and then the header. SQL may succeed in adding the
items, but fail to insert the header row. We will then have rows of items that have no
corresponding invoice header record.

INVOICE_NO CUST_NUM DATE ITEMS
---------- -------- -------- -----
~~~ ~~~ ~~~ ~~
~~~ ~~~ ~~~ ~~ The invoice header table.
345 234 7-AUG-92 88
456 453 6-SEP-92 71
234 687 5-APR-92 57
~~~ ~~~ ~~~ ~~
~~~ ~~~ ~~~ ~~
INV_HEADER

INVOICE_NO QTY DESCRIPTION VALUE
---------- --- -------------- -----
~~~ ~~~ ~~~~~~~~~ ~~~
~~~ ~~~ ~~~~~~~~~ ~~~
345 25 DC POWER SPPLY 5500 The invoice body table.
345 18 SERIAL I/O CRD 1500
345 45 SVGA CARDS 2780
234 57 486 CPUs 6745
~~~ ~~~ ~~~~~~~~~ ~~~
~~~ ~~~ ~~~~~~~~~ ~~~
INV_BODY

Figure 9.1

With transaction processing in effect, if the system fails or the program crashes, you
can discard the half completed transaction with a ROLLBACK statement. In Figure 9.2,

the state that it was in before the transaction was started. You can then re-run the
whole transaction again and if SQL succeeds in executing all the statements, make the
INSERTs permanent by issuing the COMMIT command.

INSERT INTO INV_HEADER
VALUES (440, 685, '12-NOV-93', 50); SUCCEEDS
|
|
|
|/
INSERT INTO INV_BODY
VALUES (440, 12, 'RS-422 CABLE', 345); SUCCEEDS

INSERT INTO INV_BODY SYSTEM CRASH
VALUES (440, 29, '9-PIN CONN', 45); FAILS

INSERT INTO INV_BODY
VALUES (440, 9, '487 NPX', 2475); FAILS
||||
||||
||||
||||
||||/
/

DATA VALUES AFTER CRASH.

INVOICE_NO CUST_NUM DATE ITEMS
---------- -------- --------- -----
440 685 12-NOV-93 50 The ITEMS value in
INV_HEADER table reads
50 whereas the total
QTY in INV_BODY is 12.

INVOICE_NO QTY DESCRIPTION VALUE
---------- --- -------------- -----
440 12 RS-422 CABLE 345 The other two items are
missing from INV_BODY.

Figure 9.2

9.2 "Transactions From Multiple Users":
Concurrency control.

Almost all SQL systems are used in a multi-user environment where several users
access the same data. The chapter on database security looked at the ways in which
the DBMS ensures against unauthorized access to the database, this is overall
database security. In this section, we will be looking at another aspect of security;
Ensuring that the SQL update commands of one user do not interfere with the
operations of other users.

Having multiple users access the same database at the same time can lead to a
number of potential problems. Three of the most well known of these are described
next.

9.2.1 The Lost Update Problem.

This occurs when two or more transactions have their statements interleaved by the
DBMS in a certain way. Figure 9.3 shows how this can happen. Transactions 1 and 2
are started at about the same time by different programs. The DBMS executes the
individual statements in each transaction as shown in Figure 9.3. The overall result of
this sequence of operations will be that the update performed by the Order Processing
transaction (number 1) on the STOCK field will be lost. At time=1, transaction 1 sets the
value of STOCK to STOCK-10, but does not write this to disk. The computer allocates
the next two time slices to transaction 2 which updates the value of STOCK to
STOCK+75. The STOCK value that is eventually written to disk (at time=4) by
transaction 1 is the value set by transaction 2. Transaction 1's update has in fact been
lost. In a live database, such "lost" updates result in serious database inconsistency.

9.2.2 The Temporary Update Problem.

This problem occurs when one transaction updates a table row and then cancels the
update with ROLLBACK. The temporarily updated row can be accessed by another
transaction before it is changed back to it's original value. Figure 9.4 shows the
sequence of operations that can cause the temporary update problem. Transaction 1
sets the value of STOCK to STOCK-10 and writes this to disk. The DBMS then starts
transaction 2 which reads the value of STOCK which has just been updated by
transaction 1. This value is set to STOCK+75 and written to disk by transaction 2. When
control is given back to transaction 1, it issues a ROLLBACK command (as a result of
program crash for example) and cancels the STOCK = STOCK-10 update. This means
that the value for STOCK read by transaction 2 at time=3 was incorrect. It had in fact
read the temporary value. As with the lost update problem, the temporary update
problem will also results in database inconsistency if left unchecked.

TRANSACTION 1 (Order Processing)
-------------
READ STOCK VALUE
SET STOCK = STOCK-10
WRITE STOCK VALUE
READ QTY VALUE
SET QTY = QTY+10
WRITE QTY VALUE

TRANSACTION 2 (Goods Received)
-------------
READ STOCK VALUE
SET STOCK = STOCK+75
WRITE STOCK VALUE

Two programs run these transactions at about the same time. Transaction 1 is
a part of the new order processing program and transaction 2 is a part of the
stock control system for goods received.

The DBMS might execute these transaction statements in the following order:

TIME STATEMENT TRANSACTION No.
---- --------- ---------------
0 READ STOCK VALUE Order Proc. (1)
1 SET STOCK = STOCK-10 Order Proc. (1)
2 READ STOCK VALUE Goods Recv. (2)
3 SET STOCK = STOCK+75 Goods Recv. (2)
4 WRITE STOCK VALUE Order Proc. (1)
5 READ QTY VALUE Order Proc. (1)
6 WRITE STOCK VALUE Goods Recv. (2)
7 SET QTY = QTY+10 Order Proc. (1)
8 WRITE QTY VALUE Order Proc. (1)

At time=6, transaction 2 has written an incorrect value because the update by
transaction 1 has been lost.

Figure 9.3

9.2.3 The incorrect summary problem.

This is the third common problem that can occur when multiple transactions operate on
the data without any form of concurrency control by the DBMS. It is illustrated in Figure
9.5. Transaction 4 is part of a report generating program. It reads the value of the
STOCK column for each row in the table and adds up the sum total. Transaction 3 is
part of the order processing program that handles returned products. The problem

occurs when the summing transaction reads and adds a value of a row after the order
processing transaction has changed it's value for one row (at time=6) and before the
order processing transaction has added the value of QTY for the next row (at time=8). It
results in the sum total calculated by transaction 4, being off by an amount equal to the
value of QTY from the actual sum of the STOCK column values. The incorrect
summary problem won't cause inconsistencies in the database, but will give unreliable
results in the reports. Not very good for basing important corporate decisions on.

The statements in the transactions are as described in Figure
9.3.

The DBMS might execute these transaction statements in the
following order:

---- --------- ---------------
0 READ STOCK VALUE Order Proc. (1)
1 SET STOCK = STOCK-10 Order Proc. (1)
2 WRITE STOCK VALUE Order Proc. (1)
3 READ STOCK VALUE Goods Recv. (2)
4 SET STOCK = STOCK+75 Goods Recv. (2)
5 WRITE STOCK VALUE Goods Recv. (2)
7 ROLLBACK Order Proc. (1)

Transaction 1 fails at time=7 and resets the STOCK field to
it's original value. Transaction 2 has therefore read the
temporary, incorrect value of STOCK at time=3.

Figure 9.4

9.2.4 Data Locking.

The three possible problems just described force the DBMS to implement some kind of
mechanism that prevents the updates of multiple users from interfering with each other
and from corrupting the data in the database. The DBMS must make sure that the data
in the database is consistent throughout each transaction and that it is unaffected by
transient changes made by other concurrently running transactions. The DBMS does
this by not allowing concurrent transactions to access the same rows of data at the
same time. Once a transaction accesses a row in the database, the DBMS doesn't
allow any other transaction to modify that row (they can only read it). This is done
through a technique called locking and is applied automatically by the DBMS. It is totally
transparent to the SQL user.

TRANSACTION 3 (Order Processing)
-------------
READ STOCK VALUE
SET STOCK = STOCK-10
WRITE STOCK VALUE
READ STOCK VALUE
READ QTY VALUE
SET STOCK = STOCK+QTY
WRITE STOCK VALUE

TRANSACTION 4 (Total Stock)
-------------
SUM(STOCK) FOR
ALL ITEMS

Two programs run these transactions at about the same time. Transaction 3 is a
part of the new order processing program and transaction 2 is a part of a
report generation program that calculates the sum of the STOCK row values.

The DBMS might execute these transaction statements in the following order:

---- --------- ---------------
0 SUM=0; Total Stok. (4)
1 READ STOCK (row1) Total Stok. (4)
2 SUM=SUM+STOCK (row1) Total Stok. (4)
3 READ STOCK (row2) Order Proc. (3)
4 SET STOCK = STOCK-10 (row2) Order Proc. (3)
5 WRITE STOCK VALUE (row2) Order Proc. (3)
10 READ STOCK (row3) Order Proc. (3)
12 SET STOCK = STOCK+QTY (row3) Order Proc. (3)
13 WRITE STOCK VALUE (row3) Order Proc. (3)

Transaction 4 reads the value of STOCK (row2), at time=6, after transaction 3
has subtracted 10 from it. At time=8, transaction 4 reads STOCK (row3) before
transaction 3 subtracts QTY. The sum of the STOCK field values will thus be off
by the value of QTY.

Figure 9.5

There are two basic types of locks that are used by most SQL DBMS. The share lock
and the exclusive lock. Share locks allow multiple transactions to access the data that
the lock is applied to but do not allow transactions to modify it. Share locks can be
applied by more than one transaction to the same data. The second type of basic lock is
the exclusive lock. Exclusive locks can only be applied by one transaction at a time, and
prevent all other users from locking the same data. Exclusive locks are applied when
transactions want to update data in the database and share locks are applied when
transactions want to read the data. The rules for applying share and exclusive locks is
shown in Figure 9.6.

Transaction 1

NO LOCK SHARE LOCK EXCLUSIVE LOCK

T NO LOCK yes yes yes
r
a
n SHARE yes yes no
s LOCK
a
c EXCLUSIVE yes no no
t LOCK
i
o
n

2

Figure 9.6

When you access rows of data through a transaction, the DBMS prevents other users
from modifying those rows while your transaction is still running. So if you run a
SELECT that accesses lots of rows from a table, no other user will be able to change
the values of those rows while your transaction is processing. This is why you should
keep your transactions as short as possible to maximize concurrent transaction activity
in the database.

Although locking prevents the problems associated with concurrent transactions which
we have described ie. lost update problem, temporary update problem etc, they
introduce another potential problem called a deadlock. This is illustrated in Figure 9.7.
Transaction 1 updates the STUDENTS table first then updates the EXAMS table.
Transaction 2 does the same thing, but the other way round. If the transactions are
executed by the DBMS as shown, transaction 1 updates a part of the STUDENTS table
and locks the part of it that it accesses. Transaction 2 then updates the EXAMS table
and locks part of that. Now each transaction is trying to update part of the table that has
been locked by the other transaction. The transactions are deadlocked. Such deadlocks

can also occur between three or more transactions and without external intervention,
each transaction will wait forever.

The DBMS handles deadlocks by periodically checking for them. If a deadlock is
detected, one of the transactions is arbitrarily chosen as the deadlock loser and is rolled
back thereby releasing the deadlock. This means that any transaction could be rolled
back by the DBMS at any time because it resulted in a deadlock with another
transaction. In interactive SQL, this is not much of a problem. All it means is that you will
have to re-enter the whole transaction again.

TRANSACTION 1 (T1)
-------------
UPDATE STUDENTS ROW 3
UPDATE EXAMS ROW 7

TRANSACTION 2 (T2)
-------------
UPDATE EXAMS ROW 7
UPDATE STUDENTS ROW 3

x-lock = Exclusive lock.
s-lock = Share lock.

The DBMS runs these two transactions as:

TIME STATEMENT TRANSACTION LOCK APPLIED
---- --------------------- ----------- ------------------------
0 UPDATE STUDENTS ROW 3 T1 x-lock on row3 students.
1 UPDATE EXAMS ROW 7 T2 x-lock on row7 exams.
2 UPDATE EXAMS ROW 7 T1 none, Transaction Waits.
3 UPDATE STUDENTS ROW 3 T2 none, Transaction Waits.

Transaction 1 is waiting for the row locked by transaction 2 to be released
and transaction 2 is waiting for the row locked by transaction 1 to be
released. They will both wait forever, ie. they are in a deadlock.

Figure 9.7

Chapter 10 THE DATABASE SYSTEM CATALOG

In a working SQL database, the DBMS needs to keep track of tables, columns, views,
authorization-ids and privileges. Most commercial systems rely on special relational
database tables called the system catalog for this. The system catalog or system tables
consist of tables that are created and owned by the DBMS itself. The DBMS does not
allow anyone to modify or add data to these tables, but on most systems, users can
query them and obtain information on tables, views, other users etc.

The system catalog is not defined in the ANSI/ISO standard, in fact, the standard does
not specify how SQL DBMSs should maintain the database at all. As a result of this, this
area is the most diverse amongst the commercial SQL based DBMSs. Although all the
major vendors have chosen to use the system catalog to administer the database, they
all differ in the implementation details. This chapter describes the structure and content
of a typical system catalog by looking at the system tables of some popular commercial
SQL DBMSs.

TABLES VIEWS
|---|-| |---|-|--------| :.:...:.: :.:.....:.:
| | | | | | | : : : : : : : :
| | | | | | | : : : : : : : :
|---|-| |---|-|--------| :.:...:.: :.:.....:.:

/| /| /|/| /| /|/|/| /| /| /|
| | | | | | | | | | |
C O L U M N S C O L U M N S

/ /
|| ||
||-----------------------------------||
||-----------------------------------||
ACCESS PRIVILEGES

ooooOOOOooooOOOoooOOoO
0000oooOoooOoooooooo
oooOoOooOoOoOOo
USER AUTHORIZATION-IDS

Figure 10.1

10.1 "The DBMS Needs to Manage it's Resources":
A typical system catalog.

Although the various commercial SQL systems offer widely varying features, they all
have a common base of resources that the DBMS needs to manage as a minimum
requirement. These are shown in Figure 10.1. They include:

1. Tables. All the tables in the database must be known to the DBMS. The information
must include the table's name and the table's owner.

2. Views. These are related to tables and indeed, some system catalogs have a single
table that stores details of both tables and views. Typically, the DBMS needs to know
the view's name, it's owner and the defining query.

3. Columns. The DBMS must know details of all columns for both tables and views.
Typical information includes the column name, the table or view which the column is
part of and the data type and size of the column.

4. Users. Each user has an associated authorization-id. The DBMS needs to keep track
of all the authorization-ids as well as the passwords that they are identified by.

5. Privileges. The DBMS needs to be aware of the privileges that have been granted.
Specifically, it needs to know the authorization-id of the grantor, and the grantee, the
privilege that's been granted and the table/view that the privilege was granted on.

All this information is itself stored in relational database tables. Most systems use a
different table for each of the five categories listed above.

SQL Tips

The ANSI/ISO standard does not specify any form database regulation. Almost all
commercial systems rely on the system catalog for this.

10.1.1 Table information in the system catalog.

The system table that keeps track of the details of all other tables in the database is
called SYSTABLES in IBM's OS/2 EE. The table is owned by the DBMS itself so to
query it, you must use the SYSIBM prefix. For example to list the names of all the tables
and views in the database along with the owner and the number of columns, an OS/2
EE query would be:

SELECT NAME, CREATOR, COLCOUNT
FROM SYSIBM.SYSTABLES ;

Note that NAME, CREATOR, COLCOUNT and TYPE are all fields in the SYSTABLES
system table where NAME is the name of the table or view. CREATOR is the owner of
the object. COLCOUNT is a number specifying the number of columns in the object and
TYPE is either T for tables or V for views. SYSTABLES can be queried just like any
other table, and stores it's information as other tables, in rows and columns. For
example it has multiple columns and one row of data for each table or view in the
database. You could also add a predicate to your query, as you would for any other
query, to list only the tables that are owned by FRANK for example:

SELECT NAME, CREATOR, COLCOUNT
FROM SYSIBM.SYSTABLES
WHERE TYPE = ‘T’
AND CREATOR = 'FRANK' ;

10.1.2 View information in the system catalog.

Some systems include basic information on views with the tables and store them both
in a system objects table. IBM's OS/2 EE for example has a TYPE column in
SYSTABLES to distinguish between tables and views. Other information relating
directly to views is stored in different tables. OS/2 EE has two further system catalog
tables that hold information on views. The SYSVIEWS table holds the SQL text
information that defines the view. The SYSVIEWDEP table holds the details of the base
tables (and views) that the view derives it's information from. You can also query these
like ordinary tables. For example to list the base tables/views and their owners which
the MGRS view depends on:

SELECT BNAME, BCREATOR, BTYPE
FROM SYSIBM.SYSVIEWDEP
WHERE DNAME = 'MGRS' ;

BNAME holds the name of a table/view on which the view depends. BCREATOR holds
the name of the owner and BTYPE is either 'T' for tables and 'V' for views. DNAME is
the column that holds the name of the dependent view. SYSVIEWDEP has one row for
each base object. So if the MGRS view has three tables in it's definition, there will be
three rows in SYSVIEWDEP with a DNAME value of 'MGRS'.

10.1.3 Column information in the system catalog.

Just keeping details of tables and views is not enough to manage a database. The

DBMS also needs to know about all the columns in each table and view. All the major
commercial SQL DBMSs use a system catalog table to keep track of columns. IBM's
OS/2 EE for example uses the SYSCOLUMNS table for this. This table holds the
information directly relating to the column such as the column name, data type, the
table or view it is part of etc. as well as statistical information which the DBMS uses to
optimize queries which access the column. This information is generally of no use to the
us directly, but we can query SYSCOLUMNS to extract useful information. For
example, to find the number of columns and their types in the LECTURERS table:

SELECT NAME, COLTYPE, LENGTH
FROM SYSIBM.SYSCOLUMNS
WHERE TBNAME = 'LECTURERS' ;

NAME, COLTYPE and LENGTH are fields in SYSCOLUMNS which hold details of the
name of the column, it's data type and it's size respectively. TBNAME stores the name
of the table which the column is part of.

10.1.4 User information in the system catalog.

Knowledge of all valid authorization-ids is essential if the DBMS is going to maintain
system security. Information on users is stored in a table in the system catalog. There is
usually one row per user in this table. Typical details that are stored include the
authorization-id and the associated password for each user. The nature of this
information is such that sensitive information such as user passwords is only available
to database administrators and users who have very high security clearance.

10.1.5 Privileges and other information in the system catalog.

The privileges information table holds the details of the privileges granted to users.
Typically, the DBMS stores details such as the privilege granted, the user who granted
the privilege, the user who received the privilege, the database object to which the
privilege applies, a time stamp to indicate when the privilege was granted etc.
Whenever a user enters an SQL statement, the DBMS first check if that user has the
required privileges to carry out the task.

10.1.6 Commenting the tables, views and columns.

In large, complex databases, it can become difficult to remember the exact functions of

all the tables, views and columns. Although there is no substitute for thorough design
documentation, most commercial SQL systems also allow you to attach labels and
comments to each table, view and column defined in the database. The remarks are
usually stored in the system catalog's objects definition table, eg. SYSTABLES. Labels
are attached by using the LABEL statement. Thus to add a label to the EXAMS table:

LABEL ON TABLE EXAMS
IS 'The exams taken by the students'

The label is stored with the table definition in the system catalog. You can also attach
labels to the individual columns. For example, to label the BUDGET column in the
DEPARTMENTS table:

LABEL ON COLUMN DEPARTMENTS.BUDGET
IS 'Internally allocated budget.' ;

LABEL is used to attach a short descriptive label to a table or column. The COMMENT
statement lets you add a longer description to the SYSTABLES row for the table or view
or the SYSCOLUMNS row for the column. For example, to add a remark to the GRADE
column in the LECTURERS table:

COMMENT ON LECTURERS
(GRADE IS 'Grade A is the most senior and E, the most junior')

The remark is stored in the SYSCOLUMNS table and is appended to the row for the
GRADE column. You should add labels and remarks for all but the very simplest of
database tables. This is because when you someone else refers to the structure of the
database on a few months time say, comments are an invaluable aid in finding out
exactly what information the columns store and how the inter-table relationships are
formed.

Chapter 11 EMBEDDING SQL IN A HOST LANGUAGE.

The SQL language as we have used it so far has been used as an interactive database
query language. All the queries have been typed in at the system prompt and the
DBMS executed each query and output the results immediately (or at least, while we
waited!). This is fine for ad hoc queries that will not be repeated or are run infrequently.
Almost all commercial SQL systems provide this type of interface called either
interpretive SQL or interactive SQL. This chapter looks at the other method of using
SQL, programmatic SQL.

The term programmatic SQL is used to refer to SQL statements that are used in
conjunction with another computer language called the host language.

11.1 "SQL is not a Computer Programming Language":
Why SQL needs a host language.

By itself, SQL is a very powerful query language. However, it is not a computer
programming language in the real sense of the word. For instance, it does not have any
commands that enable programs to loop or branch to different sections of the program
code, such as FOR, DO..WHILE and IF..THEN statements. In programmatic SQL,
these are provided by a host language such as Pascal, C, FORTRAN or COBOL.

There are basically two distinct methods of interfacing SQL with a host language. One
is embedded SQL and the other is through an application program interface (API).
Embedded SQL is concerned with mixing SQL statements directly with the host
language code. Embedded SQL is the subject of this chapter. The application program
interface or API is a library of DBMS functions through which the host language
program issues commands to the DBMS. The API appears to the host language
program as just another library which the linker can link into the program.

Originally, the ANSI/ISO standard supported programmatic SQL through the concept of
modules. These are SQL procedures which must be called from a separate module
language program. The standard was extended in 1989 to include embedded SQL in
the COBOL, FORTRAN, C, PL/1, Pascal and Ada host languages.

11.2 "How Embedded SQL Programs are Processed":
Compile, Bind and Link.
Figure 11.1 shows a short embedded SQL program written in the C language. The SQL
statements are highlighted for clarity. Don't worry too much if you do not fully
understand how the program operates, we will be discussing it fully in the next few
sections.

When this program is run, it asks the user for a department number. The program then
runs an SQL query which fetches the row from the DEPARTMENTS table that the
department number applies to, and displays the department name and the current
year's budget. The output from the program is shown in Figure 11.2.

Although we have chosen to write in C, other languages such as Pascal, COBOL etc.
would have been just as applicable.

The embedded SQL program, such as the one in Figure 11.1 cannot be directly
compiled by the C compiler because of the embedded SQL statements. Figure 11.3
shows the steps involved in converting the embedded SQL source code (shown in
Figure 11.1) into an executable embedded SQL program.

The sequence of operations that are illustrated in Figure 11.3 are invisible to the
program developer. Typically, the developer is only required to start the process with a
single command. This is not so different from "normal" program development, where
you would start off the compile and link process with a single MAKE command. Even
though the whole process is initiated by a single command, it follows distinct stages:

1. The embedded SQL program is passed to a precompiler. A precompiler is a software
tool provided by the SQL system vendors. It separates the embedded SQL program
code into a host language program file and the SQL statements stored in a file called
the database request module (DBRM).

2. The host language file from the precompiler is submitted to the standard language
compiler for compilation into an object file.

3. The compiled object files from the compiler are then submitted to the linker and
linked with any library routines that may be required.

4. The DBRM file from the precompiler is operated upon by a binding program which
produces an application plan for all the SQL statements. This plan tells the DBMS all it
needs to know about accessing the data in the database requested by the SQL
statements. The application plan is stored in the database.

By the end of these stages, the host language part of the program is compiled and

linked and the SQL part is parsed, validated and optimised.

When the program is run, the executable host language program will be loaded and
executed as any other program. Whenever the program needs to execute an SQL
statement, it instructs the DBMS to find and load the application plan for that statement.
The main program and the DBMS thus co-operate to run the whole embedded SQL
program.

11.3 "How SQL Statements Are Embedded":
The EXEC SQL clause.

In the embedded SQL program of Figure 11.1, the SQL statements are preceded by the
"exec sql" precompiler directive. This introducer tells the precompiler that the next
statement is an SQL command. The statement ends with a terminator. In C, this is the
semicolon character Other languages use other terminators eg. COBOL uses END-
EXEC. Although the C language is case sensitive (printf is not the same as Printf), the
SQL statement after EXEC SQL can be either upper or lower case. This is because
these statements are removed by the precompiler before they reach the C compiler so
the compiler does not see them directly.

11.4 "How SQL Talks to the Host Language":
Host language variables.

Host language variables are used to supply values or receive values from SQL. The
variables should first be declared in the program using the ANSI/ISO standard BEGIN
DECLARE SECTION and END DECLARE SECTION statements. For example, in the
program listed in Figure 11.1, the host language variables that are going to be used with
SQL are declared as:

exec sql begin declare section;
int input_val; /* User input */
int d_no; /* Department Number */
char d_name[21]; /* Department Name */
int d_head; /* Departmental Head */
float budget; /* Current Budget */
float p_budget; /* Previous Budget */
exec sql end declare section;

The host language variables must be compatible with the SQL data types which they
refer to. For example, SQL's INTEGER type corresponds to C's int type and SQL's
CHARACTER type is compatible with C's char type. Sometimes, there will not be an
exact match eg. the BUDGET field is DECIMAL and the closest match in C is the float
type.

Once they have been declared, the host language variables can be used in the SQL
statements, preceded by a colon. For example:
exec sql select dept_no, dept_name, head, budget, p_budget
from departments
where dept_no = :input_val
into :d_no, :d_name, :d_head, :budget, :p_budget;

This SELECT statement is the programmatic SELECT which has an additional INTO
clause. The variables specified in this clause are the host language variables declared
earlier. The values from the fields specified in the SELECT clause are retrieved into the
host language variables. These can then be used in the rest of the program as normal.

Keen observers might have spotted a problem that can occur with the SELECT INTO
statement. What happens if the query retrieves more than one row? The short answer
to this is that is can't. If the query retrieves more than one row, the then all the values
cannot be placed in the host language variables and the query will fail. This restriction
only applies to the SELECT INTO statement. It is possible to use queries that fetch
multiple rows in programmatic SQL by using a data object known as a cursor. This is
described in the next section.

11.5 "Handling Queries That Retrieve Multiple Rows":
The SQL cursor.
SELECT INTO can only be used with queries that output a single row. For the majority
of queries, which retrieve multiple rows, you must use a cursor to access the results
table of the query. A cursor is a logical object that is associated with a particular query.
This is similar to a view which is associated with a particular query and which derives it's
contents by running the query.

11.5.1 Selects with cursors.

Figure 11.4 shows an embedded SQL program which uses a cursor. As before, the
SQL statements are shown highlighted for clarity.

The cursor must be declared first before it can be used. In the program of Figure 11.4,
this is done by:

exec sql declare dptcurs cursor for
select dept_no, dept_name, head, budget
from departments
where dept_no < 10 ;

The cursor is named as "dptcurs" and is assigned to the query shown. This query is not

executed immediately, it is only used to define the cursor at this stage.

In order to actually execute the query, you must open the cursor. For example:
exec sql open dptcurs ;

This statement causes the query associated with the cursor called dptcurs to be
executed. The results of the query can be accessed by the FETCH statement. In the
program of Figure 11.4, this is done by:
exec sql fetch dptcurs
into :d_no, :d_name, :d_head, :budget ;

The FETCH statement retrieves the first row from the results table into the host
language variables specified and sets the row pointer to point to the next row in the
results. We placed the FETCH statement in a repeating loop because it retrieves the
next row from the results each time it is run. The do..while loop continues to fetch
results and display them until no more rows are left to be fetched. When FETCH
reaches the end of the results table, it causes the program to jump to the
"no_more_rows" label where a "No more rows" message is output. Note that although
you can move down through the results table by using the FETCH command, there is
no way of moving up through the results table. This means that when all the rows have
been fetched through a cursor, it must be closed. There is no point in tying up system
resources in maintaining an exhausted cursor. The CLOSE CURSOR command
releases the resources allocated to the cursor, for example:
exec sql close dptcurs ;

To use the dptcurs cursor again, you must be re-declare it and then open it. The output
of this program is shown in Figure 11.5

11.5.2 Deletes and Updates with cursors.

You can use cursors to delete and update data in SQL tables. Cursors by themselves
can only access data through a query. They cannot therefore be directly used with a
DELETE statement. However, programmatic SQL allows the use of a DELETE
statement with WHERE CURRENT OF clause which uses a cursor to specify the row to
delete.

Figure 11.6 shows extracts of a program that declares a cursor named finalists that
refers to all the year 3 students in the STUDENTS table. The cursor is declared and
opened in the usual way. The do..while loop near the end of the program contains a
DELETE statement which deletes all the year 3 students from STUDENTS. Here's how
it works. When the loop is executed for the first time, The FETCH statement causes the
cursor to point to the first row of the results table ie. to the first year 3 student's row. The
actual field values of this row are retrieved into the host language variables a, b, c, d, e

and f. The DELETE statement:
exec sql delete from students
where current of finalists ;

tells the DBMS to delete the row which the cursor is currently pointing to. In this case,
this is the first year 3 student's row.

When the loop is repeated, the FETCH command causes the cursor to point to the next
year 2 student's row and this too is deleted by the DELETE with WHERE CURRENT
OF clause. When the last has been deleted, the DBMS returns a code to the program
which tells it to exit the loop and jump to the appropriate program section.

We have illustrated the case of updating data via cursors by using the DELETE
statement. You can also use UPDATE in exactly the same way to modify the value of
rows on the same principle. To be updatable (by either DELETE or UPDATE), the
cursor must satisfy the same criteria as an updatable view.

main()
{
.
.
.
/* Variable declaration section */
.
.
.

/* Declare cursor */
exec sql declare finalists cursor for
select * from students
where year = 3 ;

/* Open cursor and run the query*/
exec sql open finalists ;

.
.
.
/* Any other processing */
.
.
.

/* Delete all year 3 students */
do {
exec sql fetch finalists into :a, :b, :c, :d, :e, :f ;
exec sql delete from students
where current of finalists ;
} while(1)

.
.
.
/* Any other processing */
.
.
.

/* Error handling routines */

}

Figure 11.6

11.6 "SQL Statements That Fail":
Error Handling.

You've seen in the programs of Figures 11.1 and 11.4 that we have used the error and
warning handling statements without much explanation of how they operate.

The SQL error handling allows you to identify run-time errors and warnings produced by
the DBMS and to act on them. Run-time errors are those that result from running the
program, for example if an embedded SQL statement refers to a table which does not
exist, then the DBMS will signal a run-time error. The error handling routines provided
by SQL only apply to errors and warnings produced by the DBMS. They do not deal
with the run-time errors generated through the host language.

The DBMS reports all errors and warnings to the program through a structure called the
SQL Communications Area or SQLCA. The first line of the program of Figure 11.1 is:
exec sql include sqlca;

This tells the precompiler that we will be using SQL's error handling features later in the
program. Whenever an SQL command is executed, the DBMS sets the value of a
variable, called sqlcode, in the sqlca structure to indicate that the command was
successfully executed (sqlcode set to zero), that the command failed as a result of an
error (sqlcode set to a negative value) or that the command was executed but
generated a warning (sqlcode set to a positive value).

You tell SQL how to deal with errors and warnings by the WHENEVER statement.
There are three versions of WHENEVER which correspond to the "serious error",
"warning" and "row not found" possible results of executing an SQL command. For
example, in the program of Figure 11.1:
exec sql whenever sqlerror goto error_handler;
exec sql whenever not found goto no_number;

The first statement tells the precompiler to generate code which will cause the program
to jump to the part of the program labelled as "error_handler". The second statement
causes the program to jump to the "no_number" label if any SQL command returns a
row not found warning. Note that the "row not found" is a particular type of warning
which is specified by sqlca.sqlcode having a value of 100 (positive indicates a warning
and 100 indicates the nature of the warning ie. "row not found"). The third WHENEVER
statement is:
whenever sqlwarning goto wrng_hdlr

We have not used it in our programs, but it will cause the program to jump to the label
"wrng_hdlr" whenever an SQL command generates a warning (sqlca.sqlcode has a
positive value). If we had not used the WHENEVER statements in the program of

Figure 11.4, the do..while(1) loop would have repeated forever. The fact that the
FETCH statement in this loop generates a warning when the last row has been
retrieved causes the program flow to jump from the loop to the "no_more_rows" label.

SQL Tips

The ANSI/ISO standard specifies the NOT FOUND warning, but does not specify
the particular value that must be returned.

If no WHENEVER statements are defined, the default condition is to ignore the
warnings and errors generated by the SQL commands. This is what will happen to the
warnings generated in the programs of Figure 11.1 and 11.4 because we have not used
the WHENEVER SQLWARNING commands. We could have explicitly stated this by
using CONTINUE. For example, to explicitly tell the program to ignore warnings, we
could add the line:
exec sql whenever sqlwarning continue;

It is good programming practice to add this line instead of leaving out the WHENEVER
SQLWARNING condition altogether because it clearly indicates that you wish the
warnings to be ignored and not that you've simply forgotten to add it.

SQL Tips

The SQLCODE variable, used for reporting errors and warnings is supported by
the ANSI/ISO standard and is implemented by all commercial embedded SQL
products.

11.7 "Dealing With NULL Values":
Indicator Variables.

As we have seen, NULLs are special markers that are used by SQL whenever a value
in a field is not known. The concept of NULL markers is not used in any common
programming language. Because of this, host language variables cannot directly be
assigned NULL values.

SQL Tips

The use of indicator variables in retrieving NULL values is not specified by the
ANSI/ISO standard.

In order to accommodate NULLs, the host language uses indicator variables. These are
integer type variables that are used in conjunction with regular host language variables
and indicate if the value in the host language variable is a NULL or not. For example, if
the FETCH statement in the program of Figure 11.4 could retrieve a NULL value for the
HEAD column in the DEPARTMENTS table, then we must allow for this by using an
indicator variable with the d_head host language variable. The modified FETCH would
be:
exec sql fetch dptcurs
into :d_no, :d_name, :d_headINDICATOR:n_hd, :budget ;

The variable, n_hd, is used as an indicator variable. It must first be declared just as any
other host language variable. The keyword INDICATOR tells us that n_hd is used to
indicate if d_head is set to a NULL value by the FETCH. If a NULL value is produced for
d_head, the indicator variable, n_hd will be set to a negative number. The program can
check the value of n_hd and carry out the appropriate actions (such as display a
message "No head assigned" in the column) if a NULL is detected.

Indicator variables can also be used to assign NULL values to SQL columns. The
indicator variable must first be set to a negative value and then appended to the host
language variable in the INSERT or UPDATE statement. For example, to set the
BUDGET value in the DEPARTMENTS table to NULL, the programmatic SQL
sequence of commands is:
n_bdg = -1;
exec sql insert into departments
values (:d_no, :d_name, :d_head, :budget:n_bdg, :p_budget);

The n_bdg variable is assigned a value of -1 and is then used in the SQL statement as
an indicator variable. The negative value tells the DBMS to insert a NULL value for the
BUDGET column regardless of the current value of the budget host variable.

11.8 "A Library of SQL Functions":
The SQL API.

Some commercial SQL systems take a different approach to programmatic SQL.
Rather than using embedded SQL, products such as SQL Server, SQLBase and
Oracle (which also offers embedded SQL) use the SQL application program interface.

The host language sees the application program interface (API) as just another library
of prepackaged routines and functions.

The names and syntax of the API functions vary from one DBMS to another, and from
one host language to another but most SQL APIs follow the same general principles:

- They provide an API function to make a logical connection to the SQL DBMS.

- They provide a function to send an SQL command, in the form of a text string, to
the DBMS.

- The API provides functions to check the status of the DBMS and to handle any
errors.

- The API provides the host language functions to DECLARE, OPEN, FETCH and
CLOSE cursors.

- At the end of the program, the API provides a function to enable the program to
drop the logical connection to the DBMS.

Most commercial SQL implementations provide many more API functions then the
basic ones described. Microsoft SQL Server's API for instance consists of over a
hundred different functions. A typical SQL program might only use about a dozen of
them.

Unlike the embedded SQL statements used in mainframe languages, which are stored
as an application plan, API calls are parsed, validated, optimised and executed all at run
time, rather like interactive SQL statements.

SQL Tips

Oracle's primary method of programmatic SQL is embedded SQL but it also
supports the API method through the Oracle Call Interface.

Having to parse, validate and optimise SQL statements at run time results in slower
program execution but many commercial SQL implementations incorporate additional
features that help in speeding up program execution. SQL Server for example uses the
concept of stored procedures. These are a sequence of SQL commands that are given
a name and stored in the database already parsed, validated and optimised. At run
time, the program makes API calls to execute stored procedures rather than complex
sequences of SQL commands.

APPENDIX A - The ANSI/ISO standard data types.

The ANSI/ISO standard only specifies eight data types that can be used to represent
the data stored in tables.

1. CHARACTER(len) Fixed length character string. The len
or CHAR(len) argument refers to the maximum length of the string.
All character type values must be enclosed in single
quotes ('...').

2. INTEGER Integer types are whole numbers
or INT (without a decimal point). INT types are frequently
used as row identifying columns eg. SUB_NO, LECT_NO, EXAM_NO etc.
Usually a 32-bit signed integer.

3. SMALLINT Same as INTEGER type, but used for smaller
numbers. Usually a 16-bit signed integer.

4. DECIMAL(prec, scale) Used to represent real numbers (ie.
or DEC(prec, scale) with a decimal point). The precision argument
specifies how many significant digits the number is
to have. The scale argument is optional and
specifies how many digits are to appear after the
decimal point.

5. NUMERIC(prec, scale) The same as DECIMAL type, except that the
precision argument specifies the maximum number
of digits that may be used.

6. FLOAT(prec) Floating point numbers in scientific (base 10)
notation. The precision argument specifies the minimum precision of the data.

7. REAL Same as float, but no minimum precision is
specified.

8. DOUBLE PRECISION Same as REAL, but the implementation
or DOUBLE -defined precision is greater than that for REALs.

APPENDIX B - The Sample University Administration Database.

The university administration database is used in most of the examples in this book.
The database consists of five tables:

1. The STUDENTS table, which holds details of the students in the university.

2. The LECTURERS table, which holds details of the teaching staff at the university.

3. The SUBJECTS table, which holds details of the subjects that are available.

4. The EXAMS table, which holds details of all the exams taken by the students.

5. The DEPARTMENTS table, which holds details of the various departments
(faculties).

The SQL statement used to create each table is shown below:

CREATE TABLE STUDENTS (SURNAME CHAR(15) NOT NULL,
FIRST_NAME CHAR(15),
D_O_B DATE,
STUDENT_NO INTEGER NOT NULL UNIQUE,
DEPT_NO INTEGER,
YEAR DECIMAL(2) );

INITL CHAR(4),
DEPT_NO INTEGER,
SUB_NO INTEGER,
GRADE CHAR(1),
PAY DECIMAL(6),
JOINED DATE
UNIQUE (SURNAME, LECT_NO) );

CREATE TABLE SUBJECTS (SUB_NO INTEGER NOT NULL UNIQUE,
SUB_NAME CHAR(20),
DEPT_NO INTEGER,
CREDITS NUMERIC(2),
PASS NUMERIC(2) );

CREATE TABLE EXAMS (SUB_NO INTEGER NOT NULL,
STUDENT_NO INTEGER NOT NULL,
MARK DECIMAL(3),
DATE_TAKEN DATE );

CREATE TABLE DEPARTMENTS (DEPT_NO INTEGER NOT NULL,
DEPT_NAME CHAR(20),
HEAD INTEGER,
BUDGET DECIMAL(10),
P_BUDGET DECIMAL(10),
UNIQUE (DEPT_NO) );

The contents of the university database tables are shown in Figure B.1.

------- ---------- ----- ---------- ------- ----
Duke Fitzroy 11-26-1970 1 4 2
Ayton Phil J M A 07-13-1967 3 3 1
Patel Mahesh 12-07-1970 4 2 1
Jones Gareth P Y 01-24-1970 5 2 1
Scott Gavin T J 02-20-1971 6 2 2
Baker Abu-Mia 03-13-1971 7 4 1
Brown Joseph P A 04-19-1970 8 3 3
Gyver Sue L J V 07-30-1968 11 4 2
Mulla Farook F U 10-24-1968 14 3 2
Layton Hugh 11-16-1971 15 5 1
Wickes Wendy Y Y W 12-05-1969 16 1 1

THE STUDENTS TABLE

------- ----- ------- ------- ----- ----- --- ------
Jones R A 1 1 2 E 24000 03-25-1990
Scrivens T R 2 3 1 D 31800 09-30-1986
Nizamuddin W M 3 3 4 A 86790 05-26-1969
Campbell J G 4 5 3 C 43570 02-23-1980
Ramanujan S 5 4 5 C 40900 01-01-1985
Finley G Y 6 4 5 D 34210 03-28-1960

THE LECTURERS TABLE

------ -------- ------- ------- ----
1 Mathematics 1 2 65
2 English Lit 2 1 60
3 Engineering Drwg 1 1 71
4 Basic Accounts 3 1 67
6 Organic Chemistry 5 3 57
7 Physiology 6 3 78
8 Anatomy 6 1 74
9 Electronics 1 3 71
10 Marketing 3 2 56

THE SUBJECTS TABLE

Figure B.1

------ ---------- ---- ----------
1 1 76 05-23-1984
9 1 42 05-20-1984
3 1 67 05-15-1984
2 2 52 06-05-1984
2 3 89 06-08-1984
2 3 51 05-11-1984
4 4 34 05-11-1984
10 4 49 06-26-1984
5 5 62 05-03-1984
5 6 70 05-17-1984
5 7 36 05-23-1984
5 8 52 05-20-1984
6 9 67 05-15-1984
6 10 82 06-05-1984
6 11 73 06-08-1984
7 12 27 05-11-1984
8 12 56 05-11-1984
8 13 67 06-26-1984
7 13 63 05-03-1984

THE EXAMS TABLE

------- --------- ---- ------ --------
1 Engineering 59 5780000 6200000
2 Arts & Humanities 23 753000 643000
6 Medicine 67 6895000 6932000


Figure B.1 .....continued

Sql a practical introduction

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