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Introduction to SQL from Stefan. Not yet marked up, but will go into 

 the User's Guide sometime soon.
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<chapter id="sql">
<title>SQL</title>
<abstract>
<para>
This chapter originally appeared as a part of
Stefan Simkovics' Master's Thesis.
<!-- Move this info to the bibliography
\title{{\Large Master's Thesis}\\
\vspace{1cm}
Enhancement of the ANSI SQL Implementation of PostgreSQL\\[1em]
{\normalsize written by\\[1em]}
{\large Stefan Simkovics\\
Paul Petersgasse 36\\
2384 Breitenfurt\\
AUSTRIA \\
ssimkovi@ag.or.at\\[1em]}
{\normalsize at \\[1em]}
{\large Department of Information Systems\\
Vienna University of Technology\\[1em]}
{\normalsize with support by\\[1em]}
{\large O.Univ.Prof.Dr. Georg Gottlob\\}
{\normalsize and\\}
{\large Univ.Ass. Mag. Katrin Seyr\\}}
-->
</para>
</abstract>
<para>
SQL has become one of the most popular relational query languages all
over the world.
The name "<literal>SQL</literal>" is an abbreviation for
<firstterm>Structured Query Language</firstterm>.
In 1974 Donald Chamberlin and others defined the
language SEQUEL (<firstterm>Structured English Query Language</firstterm>) at IBM
Research. This language was first implemented in an IBM
prototype called SEQUEL-XRM in 1974-75. In 1976-77 a revised version
of SEQUEL called SEQUEL/2 was defined and the name was changed to SQL
subsequently.
</para>
<para>
A new prototype called System R was developed by IBM in 1977. System R
implemented a large subset of SEQUEL/2 (now SQL) and a number of
changes were made to SQL during the project. System R was installed in
a number of user sites, both internal IBM sites and also some selected
customer sites. Thanks to the success and acceptance of System R at
those user sites IBM started to develop commercial products that
implemented the SQL language based on the System R technology.
</para>
<para>
Over the next years IBM and also a number of other vendors announced
SQL products such as SQL/DS (IBM), DB2 (IBM) ORACLE (Oracle Corp.)
DG/SQL (Data General Corp.) SYBASE (Sybase Inc.).
</para>
<para>
SQL is also an official standard now. In 1982 the American National
Standards Institute (ANSI) chartered its Database Committee X3H2 to
develop a proposal for a standard relational language. This proposal
was ratified in 1986 and consisted essentially of the IBM dialect of
SQL. In 1987 this ANSI standard was also accepted as an international
standard by the International Organization for Standardization
(ISO). This original standard version of SQL is often referred to,
informally, as "SQL/86". In 1989 the original standard was extended
and this new standard is often, again informally, referred to as
"SQL/89". Also in 1989, a related standard called {\it Database
Language Embedded SQL} was developed.
</para>
<para>
The ISO and ANSI committees have been working for many years on the
definition of a greatly expanded version of the original standard,
referred to informally as "SQL2" or "SQL/92". This version became a
ratified standard - "International Standard \mbox{ISO/IEC 9075:1992}, {\it
Database Language SQL}" - in late 1992. "SQL/92" is the version
normally meant when people refer to "the SQL standard". A detailed
description of "SQL/92" is given in \cite{date}. At the time of
writing this document a new standard informally referred to as "SQL3"
is under development. It is planned to make SQL a turing-complete
language, i.e.\ all computable queries (e.g. recursive queries) will be
possible. This is a very complex task and therefore the completion of
the new standard can not be expected before 1999.
</para>
<sect1 id="rel-model">
<title>The Relational Data Model}</title>
<para>
As mentioned before, SQL is a relational language. That means it is
based on the "relational data model" first published by E.F. Codd in
1970. We will give a formal description of the relational model in
section <xref id="formal-notion">
<!--{\it Formal Notion of the Relational Data Model}-->
but first we want to have a look at it from a more intuitive
point of view.
</para>
<para>
A {\it relational database} is a database that is perceived by its
users as a {\it collection of tables} (and nothing else but tables).
A table consists of rows and columns where each row represents a
record and each column represents an attribute of the records
contained in the table. Figure \ref{supplier} shows an example of a
database consisting of three tables:
\begin{itemize}
\item SUPPLIER is a table storing the number
(SNO), the name (SNAME) and the city (CITY) of a supplier.
\item PART is a table storing the number (PNO) the name (PNAME) and
the price (PRICE) of a part.
\item SELLS stores information about which part (PNO) is sold by which
supplier (SNO). It serves in a sense to connect the other two tables
together.
\end{itemize}
%
\begin{figure}[h]
\begin{verbatim}
SUPPLIER SNO | SNAME | CITY SELLS SNO | PNO
-----+---------+-------- -----+-----
1 | Smith | London 1 | 1
2 | Jones | Paris 1 | 2
3 | Adams | Vienna 2 | 4
4 | Blake | Rome 3 | 1
3 | 3
4 | 2
PART PNO | PNAME | PRICE 4 | 3
-----+---------+--------- 4 | 4
1 | Screw | 10
2 | Nut | 8
3 | Bolt | 15
4 | Cam | 25
\end{verbatim}
\caption{The suppliers and parts database}
\label{supplier}
\end{figure}
%
The tables PART and SUPPLIER may be regarded as {\it entities} and
SELLS may be regarded as a {\it relationship} between a particular
part and a particular supplier.
As we will see later, SQL operates on tables like the ones just
defined but before that we will study the theory of the relational
model.
\subsection{Formal Notion of the Relational Data Model}
\label{formal_notion}
The mathematical concept underlying the relational model is the
set-theoretic {\it relation} which is a subset of the Cartesian
product of a list of domains. This set-theoretic {\it relation} gives
the model its name (do not confuse it with the relationship from the {\it
Entity-Relationship model}). Formally a domain is simply a set of
values. For example the set of integers is a domain. Also the set of
character strings of length 20 and the real numbers are examples of
domains.
\begin{definition}
The {\it Cartesian} product of domains $D_{1}, D_{2},\ldots, D_{k}$ written
\mbox{$D_{1} \times D_{2} \times \ldots \times D_{k}$} is the set of
all $k$-tuples $(v_{1},v_{2},\ldots,v_{k})$ such that \mbox{$v_{1} \in
D_{1}, v_{2} \in D_{2}, \ldots, v_{k} \in D_{k}$}.
\end{definition}
For example, when we have $k=2$, $D_{1}=\{0,1\}$ and
$D_{2}=\{a,b,c\}$, then $D_{1} \times D_{2}$ is
$\{(0,a),(0,b),(0,c),(1,a),(1,b),(1,c)\}$.
%
\begin{definition}
A Relation is any subset of the Cartesian product of one or more
domains: $R \subseteq$ \mbox{$D_{1} \times D_{2} \times \ldots \times D_{k}$}
\end{definition}
%
For example $\{(0,a),(0,b),(1,a)\}$ is a relation, it is in fact a
subset of $D_{1} \times D_{2}$ mentioned above.
The members of a relation are called tuples. Each relation of some
Cartesian product \mbox{$D_{1} \times D_{2} \times \ldots \times
D_{k}$} is said to have arity $k$ and is therefore a set of $k$-tuples.
A relation can be viewed as a table (as we already did, remember
figure \ref{supplier} {\it The suppliers and parts database}) where
every tuple is represented by a row and every column corresponds to
one component of a tuple. Giving names (called attributes) to the
columns leads to the definition of a {\it relation scheme}.
%
\begin{definition}
A {\it relation scheme} $R$ is a finite set of attributes
\mbox{$\{A_{1},A_{2},\ldots,A_{k}\}$}. There is a domain $D_{i}$ for
each attribute $A_{i}, 1 \le i \le k$ where the values of the
attributes are taken from. We often write a relation scheme as
\mbox{$R(A_{1},A_{2},\ldots,A_{k})$}.
\end{definition}
{\bf Note:} A {\it relation scheme} is just a kind of template
whereas a {\it relation} is an instance of a {\it relation
scheme}. The {\it relation} consists of tuples (and can therefore be
viewed as a table) not so the {\it relation scheme}.
\subsubsection{Domains vs. Data Types}
\label{domains}
We often talked about {\it domains} in the last section. Recall that a
domain is, formally, just a set of values (e.g., the set of integers or
the real numbers). In terms of database systems we often talk of {\it
data types} instead of domains. When we define a table we have to make
a decision about which attributes to include. Additionally we
have to decide which kind of data is going to be stored as
attribute values. For example the values of SNAME from the table
SUPPLIER will be character strings, whereas SNO will store
integers. We define this by assigning a {\it data type} to each
attribute. The type of SNAME will be VARCHAR(20) (this is the SQL type
for character strings of length $\le$ 20), the type of SNO will be
INTEGER. With the assignment of a {\it data type} we also have selected
a domain for an attribute. The domain of SNAME is the set of all
character strings of length $\le$ 20, the domain of SNO is the set of
all integer numbers.
\section{Operations in the Relational Data Model}
\label{operations}
In section \ref{formal_notion} we defined the mathematical notion of
the relational model. Now we know how the data can be stored using a
relational data model but we do not know what to do with all these
tables to retrieve something from the database yet. For example somebody
could ask for the names of all suppliers that sell the part
'Screw'. Therefore two rather different kinds of notations for
expressing operations on relations have been defined:
%
\begin{itemize}
\item The {\it Relational Algebra} which is an algebraic notation,
where queries are expressed by applying specialized operators to the
relations.
\item The {\it Relational Calculus} which is a logical notation,
where queries are expressed by formulating some logical restrictions
that the tuples in the answer must satisfy.
\end{itemize}
%
\subsection{Relational Algebra}
\label{rel_alg}
The {\it Relational Algebra} was introduced by E.~F.~Codd in 1972. It
consists of a set of operations on relations:
\begin{itemize}
\item SELECT ($\sigma$): extracts {\it tuples} from a relation that
satisfy a given restriction. Let $R$ be a table that contains an attribute
$A$. $\sigma_{A=a}(R) = \{t \in R \mid t(A) = a\}$ where $t$ denotes a
tuple of $R$ and $t(A)$ denotes the value of attribute $A$ of tuple $t$.
\item PROJECT ($\pi$): extracts specified {\it attributes} (columns) from a
relation. Let $R$ be a relation that contains an attribute $X$. $\pi_{X}(R) =
\{t(X) \mid t \in R\}$, where $t(X)$ denotes the value of attribute $X$ of
tuple $t$.
\item PRODUCT ($\times$): builds the Cartesian product of two
relations. Let $R$ be a table with arity $k_{1}$ and let $S$ be a table with
arity $k_{2}$. $R\times S$ is the set of all $(k_{1}+k_{2})$-tuples
whose first $k_{1}$ components form a tuple in $R$ and whose last
$k_{2}$ components form a tuple in $S$.
\item UNION ($\cup$): builds the set-theoretic union of two
tables. Given the tables $R$ and $S$ (both must have the same arity),
the union $R \cup S$ is the set of tuples that are in $R$ or $S$ or
both.
\item INTERSECT ($\cap$): builds the set-theoretic intersection of two
tables. Given the tables $R$ and $S$, $R \cup S$ is the set of tuples
that are in $R$ and in $S$. We again require that $R$ and $S$ have the
same arity.
\item DIFFERENCE ($-$ or $\setminus$): builds the set difference of
two tables. Let $R$ and $S$ again be two tables with the same
arity. $R-S$ is the set of tuples in $R$ but not in $S$.
\item JOIN ($\Join$): connects two tables by their common
attributes. Let $R$ be a table with the attributes $A,B$ and $C$ and
let $S$ a table with the attributes $C,D$ and $E$. There is one
attribute common to both relations, the attribute $C$. $R \Join S =
\pi_{R.A,R.B,R.C,S.D,S.E}(\sigma_{R.C=S.C}(R \times S))$. What are we
doing here? We first calculate the Cartesian product $R \times
S$. Then we select those tuples whose values for the common
attribute $C$ are equal ($\sigma_{R.C = S.C}$). Now we got a table
that contains the attribute $C$ two times and we correct this by
projecting out the duplicate column.
\begin{example}
\label{join_example}
Let's have a look at the tables that are produced by evaluating the steps
necessary for a join. \\
Let the following two tables be given:
\begin{verbatim}
R A | B | C S C | D | E
---+---+--- ---+---+---
1 | 2 | 3 3 | a | b
4 | 5 | 6 6 | c | d
7 | 8 | 9
\end{verbatim}
First we calculate the Cartesian product $R \times S$ and get:
\begin{verbatim}
R x S A | B | R.C | S.C | D | E
---+---+-----+-----+---+---
1 | 2 | 3 | 3 | a | b
1 | 2 | 3 | 6 | c | d
4 | 5 | 6 | 3 | a | b
4 | 5 | 6 | 6 | c | d
7 | 8 | 9 | 3 | a | b
7 | 8 | 9 | 6 | c | d
\end{verbatim}
\pagebreak
After the selection $\sigma_{R.C=S.C}(R \times S)$ we get:
\begin{verbatim}
A | B | R.C | S.C | D | E
---+---+-----+-----+---+---
1 | 2 | 3 | 3 | a | b
4 | 5 | 6 | 6 | c | d
\end{verbatim}
To remove the duplicate column $S.C$ we project it out by the
following operation: $\pi_{R.A,R.B,R.C,S.D,S.E}(\sigma_{R.C=S.C}(R
\times S))$ and get:
\begin{verbatim}
A | B | C | D | E
---+---+---+---+---
1 | 2 | 3 | a | b
4 | 5 | 6 | c | d
\end{verbatim}
\end{example}
\item DIVIDE ($\div$): Let $R$ be a table with the attributes $A,B,C$
and $D$ and let $S$ be a table with the attributes $C$ and $D$. Then
we define the division as: $R \div S = \{t \mid \forall t_{s} \in S~
\exists t_{r} \in R$ such that
$t_{r}(A,B)=t~\wedge~t_{r}(C,D)=t_{s}\}$ where $t_{r}(x,y)$ denotes a
tuple of table $R$ that consists only of the components $x$ and
$y$. Note that the tuple $t$ only consists of the components $A$ and
$B$ of relation $R$.
\begin{example}
Given the following tables
\begin{verbatim}
R A | B | C | D S C | D
---+---+---+--- ---+---
a | b | c | d c | d
a | b | e | f e | f
b | c | e | f
e | d | c | d
e | d | e | f
a | b | d | e
\end{verbatim}
$R \div S$ is derived as
\begin{verbatim}
A | B
---+---
a | b
e | d
\end{verbatim}
\end{example}
\end{itemize}
%
For a more detailed description and definition of the relational
algebra refer to \cite{ullman} or \cite{date86}.
\begin{example}
\label{suppl_rel_alg}
Recall that we formulated all those relational operators to be able to
retrieve data from the database. Let's return to our example of
section \ref{operations} where someone wanted to know the names of all
suppliers that sell the part 'Screw'. This question can be answered
using relational algebra by the following operation:
\begin{displaymath}
\pi_{SUPPLIER.SNAME}(\sigma_{PART.PNAME='Screw'}(SUPPLIER \Join SELLS
\Join PART))
\end{displaymath}
We call such an operation a query. If we evaluate the above query
against the tables form figure \ref{supplier} {\it The suppliers and
parts database} we will obtain the following result:
\begin{verbatim}
SNAME
-------
Smith
Adams
\end{verbatim}
\end{example}
\subsection{Relational Calculus}
\label{rel_calc}
The relational calculus is based on the {first order logic}. There are
two variants of the relational calculus:
%
\begin{itemize}
\item The {\it Domain Relational Calculus} (DRC), where variables
stand for components (attributes) of the tuples.
\item The {\it Tuple Relational Calculus} (TRC), where variables stand
for tuples.
\end{itemize}
%
We want to discuss the tuple relational calculus only because it is
the one underlying the most relational languages. For a detailed
discussion on DRC (and also TRC) see \cite{date86} or \cite{ullman}.
\subsubsection{Tuple Relational Calculus}
The queries used in TRC are of the following form:
\begin{displaymath}
\{x(A) \mid F(x)\}
\end{displaymath}
where $x$ is a tuple variable $A$ is a set of attributes and $F$ is a
formula. The resulting relation consists of all tuples $t(A)$ that satisfy
$F(t)$.
\begin{example}
If we want to answer the question from example \ref{suppl_rel_alg}
using TRC we formulate the following query:
\begin{displaymath}
\begin{array}{lcll}
\{x(SNAME) & \mid & x \in SUPPLIER~\wedge & \nonumber\\
& & \exists y \in SELLS\ \exists z \in PART & (y(SNO)=x(SNO)~\wedge \nonumber\\
& & &~ z(PNO)=y(PNO)~\wedge \nonumber\\
& & &~ z(PNAME)='Screw')\} \nonumber
\end{array}
\end{displaymath}
Evaluating the query against the tables from figure \ref{supplier}
{\it The suppliers and parts database} again leads to the same result
as in example \ref{suppl_rel_alg}.
\end{example}
\subsection{Relational Algebra vs. Relational Calculus}
\label{alg_vs_calc}
The relational algebra and the relational calculus have the same {\it
expressive power} i.e.\ all queries that can be formulated using
relational algebra can also be formulated using the relational
calculus and vice versa. This was first proved by E.~F.~Codd in
1972. This proof is based on an algorithm -"Codd's reduction
algorithm"- by which an arbitrary expression of the relational
calculus can be reduced to a semantically equivalent expression of
relational algebra. For a more detailed discussion on that refer to
\cite{date86} and
\cite{ullman}.
It is sometimes said that languages based on the relational calculus
are "higher level" or "more declarative" than languages based on
relational algebra because the algebra (partially) specifies the order
of operations while the calculus leaves it to a compiler or
interpreter to determine the most efficient order of evaluation.
\section{The SQL Language}
\label{sqllanguage}
%
As most modern relational languages SQL is based on the tuple
relational calculus. As a result every query that can be formulated
using the tuple relational calculus (or equivalently, relational
algebra) can also be formulated using SQL. There are, however,
capabilities beyond the scope of relational algebra or calculus. Here
is a list of some additional features provided by SQL that are not
part of relational algebra or calculus:
\pagebreak
%
\begin{itemize}
\item Commands for insertion, deletion or modification of data.
\item Arithmetic capability: In SQL it is possible to involve
arithmetic operations as well as comparisons, e.g. $A < B + 3$. Note
that $+$ or other arithmetic operators appear neither in relational
algebra nor in relational calculus.
\item Assignment and Print Commands: It is possible to print a
relation constructed by a query and to assign a computed relation to a
relation name.
\item Aggregate Functions: Operations such as {\it average}, {\it
sum}, {\it max}, \ldots can be applied to columns of a relation to
obtain a single quantity.
\end{itemize}
%
\subsection{Select}
\label{select}
The most often used command in SQL is the SELECT statement that is
used to retrieve data. The syntax is:
\begin{verbatim}
SELECT [ALL|DISTINCT]
{ * | <expr_1> [AS <c_alias_1>] [, ...
[, <expr_k> [AS <c_alias_k>]]]}
FROM <table_name_1> [t_alias_1]
[, ... [, <table_name_n> [t_alias_n]]]
[WHERE condition]
[GROUP BY <name_of_attr_i>
[,... [, <name_of_attr_j>]] [HAVING condition]]
[{UNION | INTERSECT | EXCEPT} SELECT ...]
[ORDER BY <name_of_attr_i> [ASC|DESC]
[, ... [, <name_of_attr_j> [ASC|DESC]]]];
\end{verbatim}
Now we will illustrate the complex syntax of the SELECT statement
with various examples. The tables used for the examples are defined in
figure \ref{supplier} {\it The suppliers and parts database}.
%
\subsubsection{Simple Selects}
\begin{example}
Here are some simple examples using a SELECT statement: \\
\\
To retrieve all tuples from table PART where the attribute PRICE is
greater than 10 we formulate the following query
\begin{verbatim}
SELECT *
FROM PART
WHERE PRICE > 10;
\end{verbatim}
and get the table:
\begin{verbatim}
PNO | PNAME | PRICE
-----+---------+--------
3 | Bolt | 15
4 | Cam | 25
\end{verbatim}
%
Using "$*$" in the SELECT statement will deliver all attributes from
the table. If we want to retrieve only the attributes PNAME and PRICE
from table PART we use the statement:
\begin{verbatim}
SELECT PNAME, PRICE
FROM PART
WHERE PRICE > 10;
\end{verbatim}
\pagebreak
\noindent In this case the result is:
\begin{verbatim}
PNAME | PRICE
--------+--------
Bolt | 15
Cam | 25
\end{verbatim}
Note that the SQL SELECT corresponds to the "projection" in relational
algebra not to the "selection" (see section \ref{rel_alg} {\it
Relational Algebra}).
\\ \\
The qualifications in the WHERE clause can also be logically connected
using the keywords OR, AND and NOT:
\begin{verbatim}
SELECT PNAME, PRICE
FROM PART
WHERE PNAME = 'Bolt' AND
(PRICE = 0 OR PRICE < 15);
\end{verbatim}
will lead to the result:
\begin{verbatim}
PNAME | PRICE
--------+--------
Bolt | 15
\end{verbatim}
Arithmetic operations may be used in the {\it selectlist} and in the WHERE
clause. For example if we want to know how much it would cost if we
take two pieces of a part we could use the following query:
\begin{verbatim}
SELECT PNAME, PRICE * 2 AS DOUBLE
FROM PART
WHERE PRICE * 2 < 50;
\end{verbatim}
and we get:
\begin{verbatim}
PNAME | DOUBLE
--------+---------
Screw | 20
Nut | 16
Bolt | 30
\end{verbatim}
Note that the word DOUBLE after the keyword AS is the new title of the
second column. This technique can be used for every element of the
{\it selectlist} to assign a new title to the resulting column. This new title
is often referred to as alias. The alias cannot be used throughout the
rest of the query.
\end{example}
\subsubsection{Joins}
\begin{example} The following example shows how {\it joins} are
realized in SQL: \\ \\
To join the three tables SUPPLIER, PART and SELLS over their common
attributes we formulate the following statement:
\begin{verbatim}
SELECT S.SNAME, P.PNAME
FROM SUPPLIER S, PART P, SELLS SE
WHERE S.SNO = SE.SNO AND
P.PNO = SE.PNO;
\end{verbatim}
\pagebreak
\noindent and get the following table as a result:
\begin{verbatim}
SNAME | PNAME
-------+-------
Smith | Screw
Smith | Nut
Jones | Cam
Adams | Screw
Adams | Bolt
Blake | Nut
Blake | Bolt
Blake | Cam
\end{verbatim}
In the FROM clause we introduced an alias name for every relation
because there are common named attributes (SNO and PNO) among the
relations. Now we can distinguish between the common named attributes
by simply prefixing the attribute name with the alias name followed by
a dot. The join is calculated in the same way as shown in example
\ref{join_example}. First the Cartesian product $SUPPLIER\times PART
\times SELLS$ is derived. Now only those tuples satisfying the
conditions given in the WHERE clause are selected (i.e.\ the common
named attributes have to be equal). Finally we project out all
columns but S.SNAME and P.PNAME.
\end{example}
%
\subsubsection{Aggregate Operators}
SQL provides aggregate operators (e.g. AVG, COUNT, SUM, MIN, MAX) that
take the name of an attribute as an argument. The value of the
aggregate operator is calculated over all values of the specified
attribute (column) of the whole table. If groups are specified in the
query the calculation is done only over the values of a group (see next
section).
\begin{example}
If we want to know the average cost of all parts in table PART we use
the following query:
\begin{verbatim}
SELECT AVG(PRICE) AS AVG_PRICE
FROM PART;
\end{verbatim}
The result is:
\begin{verbatim}
AVG_PRICE
-----------
14.5
\end{verbatim}
If we want to know how many parts are stored in table PART we use
the statement:
\begin{verbatim}
SELECT COUNT(PNO)
FROM PART;
\end{verbatim}
and get:
\begin{verbatim}
COUNT
-------
4
\end{verbatim}
\end{example}
\subsubsection{Aggregation by Groups}
SQL allows to partition the tuples of a table into groups. Then the
aggregate operators described above can be applied to the groups
(i.e. the value of the aggregate operator is no longer calculated over
all the values of the specified column but over all values of a
group. Thus the aggregate operator is evaluated individually for every
group.)
\\ \\
The partitioning of the tuples into groups is done by using the
keywords \mbox{GROUP BY} followed by a list of attributes that define the
groups. If we have {\tt GROUP BY $A_{1}, \ldots, A_{k}$} we partition
the relation into groups, such that two tuples are in the same group
if and only if they agree on all the attributes $A_{1}, \ldots,
A_{k}$.
\begin{example}
If we want to know how many parts are sold by every supplier we
formulate the query:
\begin{verbatim}
SELECT S.SNO, S.SNAME, COUNT(SE.PNO)
FROM SUPPLIER S, SELLS SE
WHERE S.SNO = SE.SNO
GROUP BY S.SNO, S.SNAME;
\end{verbatim}
and get:
\begin{verbatim}
SNO | SNAME | COUNT
-----+-------+-------
1 | Smith | 2
2 | Jones | 1
3 | Adams | 2
4 | Blake | 3
\end{verbatim}
Now let's have a look of what is happening here: \\
First the join of the
tables SUPPLIER and SELLS is derived:
\begin{verbatim}
S.SNO | S.SNAME | SE.PNO
-------+---------+--------
1 | Smith | 1
1 | Smith | 2
2 | Jones | 4
3 | Adams | 1
3 | Adams | 3
4 | Blake | 2
4 | Blake | 3
4 | Blake | 4
\end{verbatim}
Next we partition the tuples into groups by putting all tuples
together that agree on both attributes S.SNO and S.SNAME:
\begin{verbatim}
S.SNO | S.SNAME | SE.PNO
-------+---------+--------
1 | Smith | 1
| 2
--------------------------
2 | Jones | 4
--------------------------
3 | Adams | 1
| 3
--------------------------
4 | Blake | 2
| 3
| 4
\end{verbatim}
In our example we got four groups and now we can apply the aggregate
operator COUNT to every group leading to the total result of the query
given above.
\end{example}
%
Note that for the result of a query using GROUP BY and aggregate
operators to make sense the attributes grouped by must also appear in
the {\it selectlist}. All further attributes not appearing in the GROUP
BY clause can only be selected by using an aggregate function. On
the other hand you can not use aggregate functions on attributes
appearing in the GROUP BY clause.
\subsubsection{Having}
The HAVING clause works much like the WHERE clause and is used to
consider only those groups satisfying the qualification given in the
HAVING clause. The expressions allowed in the HAVING clause must
involve aggregate functions. Every expression using only plain
attributes belongs to the WHERE clause. On the other hand every
expression involving an aggregate function must be put to the HAVING
clause.
\begin{example}
If we want only those suppliers selling more than one part we use the
query:
\begin{verbatim}
SELECT S.SNO, S.SNAME, COUNT(SE.PNO)
FROM SUPPLIER S, SELLS SE
WHERE S.SNO = SE.SNO
GROUP BY S.SNO, S.SNAME
HAVING COUNT(SE.PNO) > 1;
\end{verbatim}
and get:
\begin{verbatim}
SNO | SNAME | COUNT
-----+-------+-------
1 | Smith | 2
3 | Adams | 2
4 | Blake | 3
\end{verbatim}
\end{example}
\subsubsection{Subqueries}
In the WHERE and HAVING clauses the use of subqueries (subselects) is
allowed in every place where a value is expected. In this case the
value must be derived by evaluating the subquery first. The usage of
subqueries extends the expressive power of SQL.
\begin{example}
If we want to know all parts having a greater price than the part
named 'Screw' we use the query:
\begin{verbatim}
SELECT *
FROM PART
WHERE PRICE > (SELECT PRICE FROM PART
WHERE PNAME='Screw');
\end{verbatim}
The result is:
\begin{verbatim}
PNO | PNAME | PRICE
-----+---------+--------
3 | Bolt | 15
4 | Cam | 25
\end{verbatim}
When we look at the above query we can see
the keyword SELECT two times. The first one at the beginning of the
query - we will refer to it as outer SELECT - and the one in the WHERE
clause which begins a nested query - we will refer to it as inner
SELECT. For every tuple of the outer SELECT the inner SELECT has to be
evaluated. After every evaluation we know the price of the tuple named
'Screw' and we can check if the price of the actual tuple is
greater.
\\ \\
\noindent If we want to know all suppliers that do not sell any part
(e.g. to be able to remove these suppliers from the database) we use:
\begin{verbatim}
SELECT *
FROM SUPPLIER S
WHERE NOT EXISTS
(SELECT * FROM SELLS SE
WHERE SE.SNO = S.SNO);
\end{verbatim}
In our example the result will be empty because every supplier sells
at least one part. Note that we use S.SNO from the outer SELECT within
the WHERE clause of the inner SELECT. As described above the subquery
is evaluated for every tuple from the outer query i.e. the value for
S.SNO is always taken from the actual tuple of the outer SELECT.
\end{example}
\subsubsection{Union, Intersect, Except}
These operations calculate the union, intersect and set theoretic
difference of the tuples derived by two subqueries:
\begin{example}
The following query is an example for UNION:
\begin{verbatim}
SELECT S.SNO, S.SNAME, S.CITY
FROM SUPPLIER S
WHERE S.SNAME = 'Jones'
UNION
SELECT S.SNO, S.SNAME, S.CITY
FROM SUPPLIER S
WHERE S.SNAME = 'Adams';
\end{verbatim}
gives the result:
\begin{verbatim}
SNO | SNAME | CITY
-----+-------+--------
2 | Jones | Paris
3 | Adams | Vienna
\end{verbatim}
Here an example for INTERSECT:
\begin{verbatim}
SELECT S.SNO, S.SNAME, S.CITY
FROM SUPPLIER S
WHERE S.SNO > 1
INTERSECT
SELECT S.SNO, S.SNAME, S.CITY
FROM SUPPLIER S
WHERE S.SNO > 2;
\end{verbatim}
gives the result:
\begin{verbatim}
SNO | SNAME | CITY
-----+-------+--------
2 | Jones | Paris
\end{verbatim}
The only tuple returned by both parts of the query is the one having $SNO=2$.
\pagebreak
\noindent Finally an example for EXCEPT:
\begin{verbatim}
SELECT S.SNO, S.SNAME, S.CITY
FROM SUPPLIER S
WHERE S.SNO > 1
EXCEPT
SELECT S.SNO, S.SNAME, S.CITY
FROM SUPPLIER S
WHERE S.SNO > 3;
\end{verbatim}
gives the result:
\begin{verbatim}
SNO | SNAME | CITY
-----+-------+--------
2 | Jones | Paris
3 | Adams | Vienna
\end{verbatim}
\end{example}
%
\subsection{Data Definition}
\label{datadef}
%
There is a set of commands used for data definition included in the
SQL language.
\subsubsection{Create Table}
\label{create}
The most fundamental command for data definition is the
one that creates a new relation (a new table). The syntax of the
CREATE TABLE command is:
%
\begin{verbatim}
CREATE TABLE <table_name>
(<name_of_attr_1> <type_of_attr_1>
[, <name_of_attr_2> <type_of_attr_2>
[, ...]]);
\end{verbatim}
%
\begin{example}
To create the tables defined in figure \ref{supplier} the
following SQL statements are used:
\begin{verbatim}
CREATE TABLE SUPPLIER
(SNO INTEGER,
SNAME VARCHAR(20),
CITY VARCHAR(20));
CREATE TABLE PART
(PNO INTEGER,
PNAME VARCHAR(20),
PRICE DECIMAL(4 , 2));
\end{verbatim}
\begin{verbatim}
CREATE TABLE SELLS
(SNO INTEGER,
PNO INTEGER);
\end{verbatim}
\end{example}
%
\subsubsection{Data Types in SQL}
The following is a list of some data types that are supported by SQL:
\begin{itemize}
\item INTEGER: signed fullword binary integer (31 bits precision).
\item SMALLINT: signed halfword binary integer (15 bits precision).
\item DECIMAL ($p \lbrack,q\rbrack $): signed packed decimal number of $p$
digits precision with assumed $q$ of them right to the decimal
point. $(15\ge p \ge q \ge 0)$. If $q$ is omitted it is assumed to be 0.
\item FLOAT: signed doubleword floating point number.
\item CHAR($n$): fixed length character string of length $n$.
\item VARCHAR($n$): varying length character string of maximum length
$n$.
\end{itemize}
\subsubsection{Create Index}
Indices are used to speed up access to a relation. If a relation $R$
has an index on attribute $A$ then we can retrieve all tuples $t$
having $t(A) = a$ in time roughly proportional to the number of such
tuples $t$ rather than in time proportional to the size of $R$.
To create an index in SQL the CREATE INDEX command is used. The syntax
is:
\begin{verbatim}
CREATE INDEX <index_name>
ON <table_name> ( <name_of_attribute> );
\end{verbatim}
%
\begin{example}
To create an index named I on attribute SNAME of relation SUPPLIER
we use the following statement:
\begin{verbatim}
CREATE INDEX I
ON SUPPLIER (SNAME);
\end{verbatim}
\end{example}
%
The created index is maintained automatically, i.e.\ whenever a new tuple
is inserted into the relation SUPPLIER the index I is adapted. Note
that the only changes a user can percept when an index is present
are an increased speed.
\subsubsection{Create View}
A view may be regarded as a {\it virtual table}, i.e.\ a table that
does not {\it physically} exist in the database but looks to the user
as if it did. By contrast, when we talk of a {\it base table} there is
really a physically stored counterpart of each row of the table
somewhere in the physical storage.
Views do not have their own, physically separate, distinguishable
stored data. Instead, the system stores the {\it definition} of the
view (i.e.\ the rules about how to access physically stored {\it base
tables} in order to materialize the view) somewhere in the {\it system
catalogs} (see section \ref{catalogs} {\it System Catalogs}). For a
discussion on different techniques to implement views refer to section
\ref{view_impl} {\it Techniques To Implement Views}.
In SQL the CREATE VIEW command is used to define a view. The syntax
is:
\begin{verbatim}
CREATE VIEW <view_name>
AS <select_stmt>
\end{verbatim}
where {\tt $<$select\_stmt$>$ } is a valid select statement as defined
in section \ref{select}. Note that the {\tt $<$select\_stmt$>$ } is
not executed when the view is created. It is just stored in the {\it
system catalogs} and is executed whenever a query against the view is
made.
\begin{example} Let the following view definition be given (we use
the tables from figure \ref{supplier} {\it The suppliers and parts
database} again):
\begin{verbatim}
CREATE VIEW London_Suppliers
AS SELECT S.SNAME, P.PNAME
FROM SUPPLIER S, PART P, SELLS SE
WHERE S.SNO = SE.SNO AND
P.PNO = SE.PNO AND
S.CITY = 'London';
\end{verbatim}
Now we can use this {\it virtual relation} {\tt London\_Suppliers} as
if it were another base table:
\begin{verbatim}
SELECT *
FROM London_Suppliers
WHERE P.PNAME = 'Screw';
\end{verbatim}
will return the following table:
\begin{verbatim}
SNAME | PNAME
-------+-------
Smith | Screw
\end{verbatim}
To calculate this result the database system has to do a {\it hidden}
access to the base tables SUPPLIER, SELLS and PART first. It
does so by executing the query given in the view definition against
those base tables. After that the additional qualifications (given in the
query against the view) can be applied to obtain the resulting table.
\end{example}
\subsubsection{Drop Table, Drop Index, Drop View}
To destroy a table (including all tuples stored in that table) the
DROP TABLE command is used:
\begin{verbatim}
DROP TABLE <table_name>;
\end{verbatim}
%
\begin{example}
To destroy the SUPPLIER table use the following statement:
\begin{verbatim}
DROP TABLE SUPPLIER;
\end{verbatim}
\end{example}
%
The DROP INDEX command is used to destroy an index:
\begin{verbatim}
DROP INDEX <index_name>;
\end{verbatim}
%
Finally to destroy a given view use the command DROP VIEW:
\begin{verbatim}
DROP VIEW <view_name>;
\end{verbatim}
\subsection{Data Manipulation}
%
\subsubsection{Insert Into}
Once a table is created (see section \ref{create}), it can be filled
with tuples using the command INSERT INTO. The syntax is:
\begin{verbatim}
INSERT INTO <table_name> (<name_of_attr_1>
[, <name_of_attr_2> [,...]])
VALUES (<val_attr_1>
[, <val_attr_2> [, ...]]);
\end{verbatim}
%
\begin{example}
To insert the first tuple into the relation SUPPLIER of figure
\ref{supplier} {\it The suppliers and parts database} we use the
following statement:
\begin{verbatim}
INSERT INTO SUPPLIER (SNO, SNAME, CITY)
VALUES (1, 'Smith', 'London');
\end{verbatim}
%
To insert the first tuple into the relation SELLS we use:
\begin{verbatim}
INSERT INTO SELLS (SNO, PNO)
VALUES (1, 1);
\end{verbatim}
\end{example}
\subsubsection{Update}
To change one or more attribute values of tuples in a relation the
UPDATE command is used. The syntax is:
\begin{verbatim}
UPDATE <table_name>
SET <name_of_attr_1> = <value_1>
[, ... [, <name_of_attr_k> = <value_k>]]
WHERE <condition>;
\end{verbatim}
%
\begin{example}
To change the value of attribute PRICE of the part 'Screw' in the
relation PART we use:
\begin{verbatim}
UPDATE PART
SET PRICE = 15
WHERE PNAME = 'Screw';
\end{verbatim}
The new value of attribute PRICE of the tuple whose name is 'Screw' is
now 15.
\end{example}
\subsubsection{Delete}
To delete a tuple from a particular table use the command DELETE
FROM. The syntax is:
\begin{verbatim}
DELETE FROM <table_name>
WHERE <condition>;
\end{verbatim}
\begin{example}
To delete the supplier called 'Smith' of the table SUPPLIER the
following statement is used:
\begin{verbatim}
DELETE FROM SUPPLIER
WHERE SNAME = 'Smith';
\end{verbatim}
\end{example}
%
\subsection{System Catalogs}
\label{catalogs}
In every SQL database system {\it system catalogs} are used to keep
track of which tables, views indexes etc. are defined in the
database. These system catalogs can be queried as if they were normal
relations. For example there is one catalog used for the definition of
views. This catalog stores the query from the view definition. Whenever
a query against a view is made, the system first gets the {\it
view-definition-query} out of the catalog and materializes the view
before proceeding with the user query (see section \ref{view_impl}
{\it Techniques To Implement Views} for a more detailed
description). For more information about {\it system catalogs} refer to
\cite{date}.
\subsection{Embedded SQL}
In this section we will sketch how SQL can be embedded into a host
language (e.g.\ C). There are two main reasons why we want to use SQL
from a host language:
%
\begin{itemize}
\item There are queries that cannot be formulated using pure SQL
(i.e. recursive queries). To be able to perform such queries we need a
host language with a greater expressive power than SQL.
\item We simply want to access a database from some application that
is written in the host language (e.g.\ a ticket reservation system
with a graphical user interface is written in C and the information
about which tickets are still left is stored in a database that can be
accessed using embedded SQL).
\end{itemize}
%
A program using embedded SQL in a host language consists of statements
of the host language and of embedded SQL (ESQL) statements. Every ESQL
statement begins with the keywords EXEC SQL. The ESQL statements are
transformed to statements of the host language by a {\it precompiler}
(mostly calls to library routines that perform the various SQL
commands).
When we look at the examples throughout section \ref{select} we
realize that the result of the queries is very often a set of
tuples. Most host languages are not designed to operate on sets so we
need a mechanism to access every single tuple of the set of tuples
returned by a SELECT statement. This mechanism can be provided by
declaring a {\it cursor}. After that we can use the FETCH command to
retrieve a tuple and set the cursor to the next tuple.
\\ \\
For a detailed discussion on embedded SQL refer to \cite{date},
\cite{date86} or \cite{ullman}.
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