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SQL Tutorial

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SQL Tutorial

SQL Tutorial

SQL – Structured Query Lhttps://www.blogger.com/blogger.g?blogID=6490856755596534642#editor/target=post;postID=8914472568819753379anguage
1.1 Tables
In relational database systems (DBS) data are represented using tables (relations). A query issued against the DBS also results in a table. A table has the following structure:
Column 1 Column 2 ... Column n
←− Tuple (or Record)
... ... ... ...
A table is uniquely identiﬁed by its name and consists of rows that contain the stored informa- tion, each row containing exactly one tuple (or record). A table can have one or more columns. A column is made up of a column name and a data type, and it describes an attribute of the tuples. The structure of a table, also called relation schema, thus is deﬁned by its attributes. The type of information to be stored in a table is deﬁned by the data types of the attributes at table creation time.
SQL uses the terms table, row, andcolumn for relation, tuple, andattribute, respectively. In this tutorial we will use the terms interchangeably.
A table can have up to 254 columns which may have diﬀerent or same data types and sets of values (domains), respectively. Possible domains are alphanumeric data (strings), numbers and date formats. Oracle oﬀers the following basic data types:
• char(n): Fixed-length character data (string), n characters long. The maximum size for n is 255 bytes (2000 in Oracle8). Note that a string of type char is always padded on right with blanks to full length of n.(☞ can be memory consuming). Example: char(40) • varchar2(n): Variable-length character string. The maximum size for n is 2000 (4000 in Oracle8). Only the bytes used for a string require storage. Example: varchar2(80) • number(o,d): Numeric data type for integers and reals. o = overall number of digits, d = number of digits to the right of the decimal point. Maximum values: o =38, d= −84 to +127. Examples: number(8), number(5,2) Note that, e.g., number(5,2) cannot contain anything larger than 999.99 without result- ing in an error. Data types derived from number are int[eger], dec[imal], smallint and real. • date: Date data type for storing date and time. The default format for a date is: DD-MMM-YY. Examples: ’13-OCT-94’, ’07-JAN-98’
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• long: Character data up to a length of 2GB. Only one long column is allowed per table.
Note: In Oracle-SQL there is no data typeboolean. It can, however, be simulated by using either char(1) or number(1).
As long as no constraint restricts the possible values of an attribute, it may have the special value null (for unknown). This value is diﬀerent from the number 0, and it is also diﬀerent from the empty string ’’.
Further properties of tables are:
• the order in which tuples appear in a table is not relevant (unless a query requires an explicit sorting). • a table has no duplicate tuples (depending on the query, however, duplicate tuples can appear in the query result).
A database schema is a set of relation schemas. The extension of a database schema at database run-time is called a database instance or database, for short.
1.1.1 Example Database
In the following discussions and examples we use an example database to manage information about employees, departments and salary scales. The corresponding tables can be created under the UNIX shell using the command demobld. The tables can be dropped by issuing the command demodrop under the UNIX shell.
The table EMP is used to store information about employees:
EMPNO ENAME JOB MGR HIREDATE SAL DEPTNO 7369 SMITH CLERK 7902 17-DEC-80 800 20 7499 ALLEN SALESMAN 7698 20-FEB-81 1600 30 7521 WARD SALESMAN 7698 22-FEB-81 1250 30 ........................................................... 7698 BLAKE MANAGER 01-MAY-81 3850 30 7902 FORD ANALYST 7566 03-DEC-81 3000 10
For the attributes, the following data types are deﬁned:
EMPNO:number(4), ENAME:varchar2(30), JOB:char(10), MGR:number(4), HIREDATE:date, SAL:number(7,2), DEPTNO:number(2)
Each row (tuple) from the table is interpreted as follows: an employee has a number, a name, a job title and a salary. Furthermore, for each employee the number of his/her manager, the date he/she was hired, and the number of the department where he/she is working are stored.
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The table DEPT stores information about departments (number, name, and location):
DEPTNO DNAME LOC 10 STORE CHICAGO 20 RESEARCH DALLAS 30 SALES NEW YORK 40 MARKETING BOSTON
Finally, the table SALGRADE contains all information about the salary scales, more precisely, the maximum and minimum salary of each scale.
GRADE LOSAL HISAL 1 700 1200 2 1201 1400 3 1401 2000 4 2001 3000 5 3001 9999
1.2 Queries (Part I)
In order to retrieve the information stored in the database, the SQL query language is used. In the following we restrict our attention to simple SQL queries and defer the discussion of more complex queries to Section 1.5
In SQL a query has the following (simpliﬁed) form (components in brackets [ ] are optional):
select [distinct] <column(s)> from <table> [ where <condition> ] [ order by <column(s) [asc|desc]> ]
1.2.1 Selecting Columns
The columns to be selected from a table are speciﬁed after the keyword select. This operation is also called projection. For example, the query
select LOC, DEPTNO from DEPT;
lists only the number and the location for each tuple from the relation DEPT. If all columns should be selected, the asterisk symbol “∗” can be used to denote all attributes. The query select ∗ from EMP; retrieves all tuples with all columns from the table EMP. Instead of an attribute name, the select clause may also contain arithmetic expressions involving arithmetic operators etc. select ENAME, DEPTNO, SAL ∗ 1.55 from EMP;
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For the diﬀerent data types supported in Oracle, several operators and functions are provided: • for numbers: abs, cos, sin, exp, log, power, mod, sqrt,+ ,−,∗,/,... • for strings: chr, concat(string1, string2), lower, upper, replace(string, search string, replacement string), translate, substr(string, m, n), length, to date,... • for the date data type: add month, month between, next day, to char,...
The usage of these operations is described in detail in the SQL*Plus help system (see also Section 2).
Consider the query
select DEPTNO from EMP;
which retrieves the department number for each tuple. Typically, some numbers will appear more than only once in the query result, that is, duplicate result tuples are not automatically eliminated. Inserting the keyword distinct after the keyword select, however, forces the elimination of duplicates from the query result.
It is also possible to specify a sorting order in which the result tuples of a query are displayed. For this the order by clause is used and which has one or more attributes listed in the select clause as parameter. desc speciﬁes a descending order and asc speciﬁes an ascending order (this is also the default order). For example, the query
select ENAME, DEPTNO, HIREDATE from EMP; from EMP order by DEPTNO [asc], HIREDATE desc;
displays the result in an ascending order by the attribute DEPTNO. If two tuples have the same attribute value for DEPTNO, the sorting criteria is a descending order by the attribute values of HIREDATE. For the above query, we would get the following output:
ENAME DEPTNO HIREDATE FORD 10 03-DEC-81 SMITH 20 17-DEC-80 BLAKE 30 01-MAY-81 WARD 30 22-FEB-81 ALLEN 30 20-FEB-81 ...........................
1.2.2 Selection of Tuples
Up to now we have only focused on selecting (some) attributes of all tuples from a table. If one is interested in tuples that satisfy certain conditions, the where clause is used. In a where clause simple conditions based on comparison operators can be combined using the logical connectives and, or, andnot to form complex conditions. Conditions may also include pattern matching operations and even subqueries (Section 1.5).
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Example: List the job title and the salary of those employees whose manager has the number 7698 or 7566 and who earn more than 1500: select JOB, SAL from EMP where (MGR = 7698 or MGR = 7566) and SAL > 1500;
For all data types, the comparison operators =,!= or <>,<, >,<=, => are allowed in the conditions of a where clause.
Further comparison operators are: • Set Conditions: <column> [not] in (<list of values>) Example: select ∗ from DEPT where DEPTNO in (20,30); • Null value: <column> is [not] null, i.e., for a tuple to be selected there must (not) exist a deﬁned value for this column. Example: select ∗ from EMP where MGR is not null; Note: the operations = null and ! = null are not deﬁned! • Domain conditions: <column> [not] between <lower bound> and <upper bound> Example: • select EMPNO, ENAME, SAL from EMP where SAL between 1500 and 2500; • select ENAME from EMP where HIREDATE between ’02-APR-81’ and ’08-SEP-81’;
1.2.3 String Operations
In order to compare an attribute with a string, it is required to surround the string by apos- trophes, e.g., where LOCATION = ’DALLAS’. A powerful operator for pattern matching is the like operator. Together with this operator, two special characters are used: the percent sign % (also called wild card), and the underline , also called position marker. For example, if one is interested in all tuples of the table DEPT that contain two C in the name of the depart- ment, the condition would be where DNAME like ’%C%C%’. The percent sign means that any (sub)string is allowed there, even the empty string. In contrast, the underline stands for exactly one character. Thus the condition where DNAME like ’%C C%’ would require that exactly one character appears between the two Cs. To test for inequality, the not clause is used.
Further string operations are: • upper(<string>) takes a string and converts any letters in it to uppercase, e.g., DNAME = upper(DNAME)(The name of a department must consist only of upper case letters.) • lower(<string>) converts any letter to lowercase, • initcap(<string>) converts the initial letter of every word in <string> to uppercase. • length(<string>) returns the length of the string. • substr(<string>, n [, m]) clips out a m character piece of <string>, starting at position n. Ifm is not speciﬁed, the end of the string is assumed. substr(’DATABASE SYSTEMS’, 10, 7) returns the string ’SYSTEMS’.
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1.2.4 Aggregate Functions
Aggregate functions are statistical functions such as count, min, max etc. They are used to compute a single value from a set of attribute values of a column:
count Counting Rows Example: How many tuples are stored in the relation EMP? select count(∗) from EMP; Example: How many diﬀerent job titles are stored in the relation EMP? select count(distinct JOB) from EMP; max Maximum value for a column min Minimum value for a column Example: List the minimum and maximum salary. select min(SAL), max(SAL) from EMP; Example: Compute the diﬀerence between the minimum and maximum salary. select max(SAL)-min(SAL) from EMP; sum Computes the sum of values (only applicable to the data type number) Example: Sum of all salaries of employees working in the department 30. select sum(SAL) from EMP where DEPTNO = 30; avg Computes average value for a column (only applicable to the data type number)
Note: avg, min and max ignore tuples that have a null value for the speciﬁed attribute, but count considers null values.
1.3 Data Deﬁnition in SQL
1.3.1 Creating Tables
The SQL command for creating an empty table has the following form:
create table <table> ( <column 1><data type> [not null][unique][<column constraint>], ......... <column n><data type> [not null][unique][<column constraint>], [<table constraint(s)>] );
For each column, a name and a data type must be speciﬁed and the column name must be unique within the table deﬁnition. Column deﬁnitions are separated by colons. There is no diﬀerence between names in lower case letters and names in upper case letters. In fact, the only place where upper and lower case letters matter are strings comparisons. A not null
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constraint is directly speciﬁed after the data type of the column and the constraint requires deﬁned attribute values for that column, diﬀerent from null.
The keyword unique speciﬁes that no two tuples can have the same attribute value for this column. Unless the condition not null is also speciﬁed for this column, the attribute value null is allowed and two tuples having the attribute value null for this column do not violate the constraint.
Example: The create table statement for our EMP table has the form create table EMP ( EMPNO number(4) not null, ENAME varchar2(30) not null, JOB varchar2(10), MGR number(4), HIREDATE date, SAL number(7,2), DEPTNO number(2) ); Remark: Except for the columns EMPNO and ENAME null values are allowed.
1.3.2 Constraints
The deﬁnition of a table may include the speciﬁcation of integrity constraints. Basically two types of constraints are provided: column constraints are associated with a single column whereas table constraints are typically associated with more than one column. However, any column constraint can also be formulated as a table constraint. In this section we consider only very simple constraints. More complex constraints will be discussed in Section 5.1.
The speciﬁcation of a (simple) constraint has the following form: [constraint <name>] primary key | unique | not null A constraint can be named. It is advisable to name a constraint in order to get more meaningful information when this constraint is violated due to, e.g., an insertion of a tuple that violates the constraint. If no name is speciﬁed for the constraint, Oracle automatically generates a name of the pattern SYS C<number>.
The two most simple types of constraints have already been discussed: not null and unique. Probably the most important type of integrity constraints in a database are primary key con- straints. A primary key constraint enables a unique identiﬁcation of each tuple in a table. Based on a primary key, the database system ensures that no duplicates appear in a table. For example, for our EMP table, the speciﬁcation
create table EMP ( EMPNO number(4) constraint pk emp primary key, ...);
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deﬁnes the attribute EMPNO as the primary key for the table. Each value for the attribute EMPNO thus must appear only once in the table EMP. A table, of course, may only have one primary key. Note that in contrast to a unique constraint, null values are not allowed.
Example: We want to create a table called PROJECT to store information about projects. For each project, we want to store the number and the name of the project, the employee number of the project’s manager, the budget and the number of persons working on the project, and the start date and end date of the project. Furthermore, we have the following conditions: - a project is identiﬁed by its project number, - the name of a project must be unique, - the manager and the budget must be deﬁned.
Table deﬁnition: create table PROJECT ( PNO number(3) constraint prj pk primary key, PNAME varchar2(60) unique, PMGR number(4) not null, PERSONS number(5), BUDGET number(8,2) not null, PSTART date, PEND date);
A unique constraint can include more than one attribute. In this case the pattern unique(<column i>,...,<column j>) is used. If it is required, for example, that no two projects have the same start and end date, we have to add the table constraint
constraint no same dates unique(PEND, PSTART)
This constraint has to be deﬁned in the create table command after both columns PEND and PSTART have been deﬁned. A primary key constraint that includes more than only one column can be speciﬁed in an analogous way.
Instead of a not null constraint it is sometimes useful to specify a default value for an attribute if no value is given, e.g., when a tuple is inserted. For this, we use the default clause.
Example: If no start date is given when inserting a tuple into the table PROJECT, the project start date should be set to January 1st, 1995:
PSTART date default(’01-JAN-95’)
Note: Unlike integrity constraints, it is not possible to specify a name for a default.
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1.3.3 Checklist for Creating Tables
The following provides a small checklist for the issues that need to be considered before creating a table. • What are the attributes of the tuples to be stored? What are the data types of the attributes? Should varchar2 be used instead of char ? • Which columns build the primary key? • Which columns do (not) allow null values? Which columns do (not) allow duplicates ? • Are there default values for certain columns that allow null values ?
1.4 Data Modiﬁcations in SQL
After a table has been created using the create table command, tuples can be inserted into the table, or tuples can be deleted or modiﬁed.
1.4.1 Insertions
The most simple way to insert a tuple into a table is to use the insert statement insert into <table> [(<column i, ..., column j>)] values (<value i, ..., value j>);
For each of the listed columns, a corresponding (matching) value must be speciﬁed. Thus an insertion does not necessarily have to follow the order of the attributes as speciﬁed in the create table statement. If a column is omitted, the value null is inserted instead. If no column list is given, however, for each column as deﬁned in the create table statement a value must be given.
Examples: insert into PROJECT(PNO, PNAME, PERSONS, BUDGET, PSTART) values(313, ’DBS’, 4, 150000.42, ’10-OCT-94’);
or
insert into PROJECT values(313, ’DBS’, 7411, null, 150000.42, ’10-OCT-94’, null);
If there are already some data in other tables, these data can be used for insertions into a new table. For this, we write a query whose result is a set of tuples to be inserted. Such an insert statement has the form insert into <table> [(<column i, ..., column j>)] <query>
Example: Suppose we have deﬁned the following table:
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create table OLDEMP ( ENO number(4) not null, HDATE date); We now can use the table EMP to insert tuples into this new relation: insert into OLDEMP (ENO, HDATE) select EMPNO, HIREDATE from EMP where HIREDATE < ’31-DEC-60’;
1.4.2 Updates
For modifying attribute values of (some) tuples in a table, we use the update statement:
update <table> set <column i> = <expression i>,...,<column j> = <expression j> [where <condition>];
An expression consists of either a constant (new value), an arithmetic or string operation, or an SQL query. Note that the new value to assign to <column i> must a the matching data type.
An update statement without a where clause results in changing respective attributes of all tuples in the speciﬁed table. Typically, however, only a (small) portion of the table requires an update.
Examples: • The employee JONES is transfered to the department 20 as a manager and his salary is increased by 1000:
update EMP set JOB = ’MANAGER’, DEPTNO = 20, SAL = SAL +1000 where ENAME = ’JONES’; • All employees working in the departments 10 and 30 get a 15% salary increase. update EMP set SAL = SAL ∗ 1.15 where DEPTNO in (10,30); Analogous to the insert statement, other tables can be used to retrieve data that are used as new values. In such a case we have a <query> instead of an <expression>.
Example: All salesmen working in the department 20 get the same salary as the manager who has the lowest salary among all managers.
update EMP set SAL =(select min(SAL) from EMP where JOB = ’MANAGER’) where JOB = ’SALESMAN’ and DEPTNO = 20; Explanation: The query retrieves the minimum salary of all managers. This value then is assigned to all salesmen working in department 20.
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It is also possible to specify a query that retrieves more than only one value (but still only one tuple!). In this case the set clause has the form set(<column i, ..., column j>)=<query>. It is important that the order of data types and values of the selected row exactly correspond to the list of columns in the set clause.
1.4.3 Deletions
All or selected tuples can be deleted from a table using the delete command:
delete from <table> [where <condition>];
If the where clause is omitted, all tuples are deleted from the table. An alternative command for deleting all tuples from a table is the truncate table <table> command. However, in this case, the deletions cannot be undone (see subsequent Section 1.4.4).
Example: Delete all projects (tuples) that have been ﬁnished before the actual date (system date):
delete from PROJECT where PEND < sysdate;
sysdate is a function in SQL that returns the system date. Another important SQL function is user, which returns the name of the user logged into the current Oracle session.
1.4.4 Commit and Rollback
A sequence of database modiﬁcations, i.e., a sequence of insert, update, anddelete state- ments, is called a transaction. Modiﬁcations of tuples are temporarily stored in the database system. They become permanent only after the statement commit; has been issued.
As long as the user has not issued the commit statement, it is possible to undo all modiﬁcations since the last commit. To undo modiﬁcations, one has to issue the statement rollback;.
It is advisable to complete each modiﬁcation of the database with a commit (as long as the modiﬁcation has the expected eﬀect). Note that any data deﬁnition command such as create table results in an internal commit.Acommit is also implicitly executed when the user terminates an Oracle session.
1.5 Queries (Part II)
In Section 1.2 we have only focused on queries that refer to exactly one table. Furthermore, conditions in a where were restricted to simple comparisons. A major feature of relational databases, however, is to combine (join) tuples stored in diﬀerent tables in order to display more meaningful and complete information. In SQL the select statement is used for this kind of queries joining relations:
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select [distinct][<alias ak>.]<column i>,...,[<alias al>.]<column j> from <table 1> [<alias a1>],...,<table n> [<alias an>] [where <condition>]
The speciﬁcation of table aliases in the from clause is necessary to refer to columns that have the same name in diﬀerent tables. For example, the column DEPTNO occurs in both EMP and DEPT. If we want to refer to either of these columns in the where or select clause, a table alias has to be speciﬁed and put in the front of the column name. Instead of a table alias also the complete relation name can be put in front of the column such as DEPT.DEPTNO, but this sometimes can lead to rather lengthy query formulations.
1.5.1 Joining Relations
Comparisons in the where clause are used to combine rows from the tables listed in the from clause.
Example: In the table EMP only the numbers of the departments are stored, not their name. For each salesman, we now want to retrieve the name as well as the number and the name of the department where he is working:
select ENAME, E.DEPTNO, DNAME from EMP E, DEPT D where E.DEPTNO = D.DEPTNO and JOB = ’SALESMAN’;
Explanation: E and D are table aliases for EMP and DEPT, respectively. The computation of the query result occurs in the following manner (without optimization): 1. Each row from the table EMP is combined with each row from the table DEPT (this oper- ation is called Cartesian product). If EMP contains m rows and DEPT contains n rows, we thus get n∗m rows. 2. From these rows those that have the same department number are selected (where E.DEPTNO = D.DEPTNO). 3. From this result ﬁnally all rows are selected for which the condition JOB = ’SALESMAN’ holds.
In this example the joining condition for the two tables is based on the equality operator “=”. The columns compared by this operator are called join columns and the join operation is called an equijoin.
Any number of tables can be combined in a select statement.
Example: For each project, retrieve its name, the name of its manager, and the name of the department where the manager is working: select ENAME, DNAME, PNAME from EMP E, DEPT D, PROJECT P where E.EMPNO = P.MGR and D.DEPTNO = E.DEPTNO;
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It is even possible to join a table with itself:
Example: List the names of all employees together with the name of their manager: select E1.ENAME, E2.ENAME from EMP E1, EMP E2 where E1.MGR = E2.EMPNO;
Explanation: The join columns are MGR for the table E1 and EMPNO for the table E2. The equijoin comparison is E1.MGR = E2.EMPNO.
1.5.2 Subqueries
Up to now we have only concentrated on simple comparison conditions in a where clause, i.e., we have compared a column with a constant or we have compared two columns. As we have already seen for the insert statement, queries can be used for assignments to columns. A query result can also be used in a condition of a where clause. In such a case the query is called a subquery and the complete select statement is called a nested query.
A respective condition in the where clause then can have one of the following forms: 1. Set-valued subqueries <expression> [not] in (<subquery>) <expression><comparison operator> [any|all](<subquery>) An <expression> can either be a column or a computed value. 2. Test for (non)existence [not] exists (<subquery>)
In a where clause conditions using subqueries can be combined arbitrarily by using the logical connectives and and or.
Example: List the name and salary of employees of the department 20 who are leading a project that started before December 31, 1990:
select ENAME, SAL from EMP where EMPNO in (select PMGR from PROJECT where PSTART < ’31-DEC-90’) and DEPTNO =20;
Explanation: The subquery retrieves the set of those employees who manage a project that started before December 31, 1990. If the employee working in department 20 is contained in this set (in operator), this tuple belongs to the query result set.
Example: List all employees who are working in a department located in BOSTON:
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select ∗ from EMP where DEPTNO in (select DEPTNO from DEPT where LOC = ’BOSTON’);
The subquery retrieves only one value (the number of the department located in Boston). Thus it is possible to use “=” instead of in. As long as the result of a subquery is not known in advance, i.e., whether it is a single value or a set, it is advisable to use the in operator.
A subquery may use again a subquery in its where clause. Thus conditions can be nested arbitrarily. An important class of subqueries are those that refer to its surrounding (sub)query and the tables listed in the from clause, respectively. Such type of queries is called correlated subqueries.
Example: List all those employees who are working in the same department as their manager (note that components in [ ] are optional:
select ∗ from EMP E1 where DEPTNO in (select DEPTNO from EMP [E] where [E.]EMPNO = E1.MGR);
Explanation: The subquery in this example is related to its surrounding query since it refers to the column E1.MGR. A tuple is selected from the table EMP (E1) for the query result if the value for the column DEPTNO occurs in the set of values select in the subquery. One can think of the evaluation of this query as follows: For each tuple in the table E1, the subquery is evaluated individually. If the condition where DEPTNO in ...evaluates to true, this tuple is selected. Note that an alias for the table EMP in the subquery is not necessary since columns without a preceding alias listed there always refer to the innermost query and tables. Conditions of the form <expression><comparison operator> [any|all] <subquery> are used to compare a given <expression> with each value selected by <subquery>. • For the clause any, the condition evaluates to true if there exists at least on row selected by the subquery for which the comparison holds. If the subquery yields an empty result set, the condition is not satisﬁed. • For the clause all, in contrast, the condition evaluates to true if for all rows selected by the subquery the comparison holds. In this case the condition evaluates to true if the subquery does not yield any row or value.
Example: Retrieve all employees who are working in department 10 and who earn at least as much as any (i.e., at least one) employee working in department 30: select ∗ from EMP where SAL >= any (select SAL from EMP where DEPTNO = 30) and DEPTNO = 10;
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Note: Also in this subquery no aliases are necessary since the columns refer to the innermost from clause.
Example: List all employees who are not working in department 30 and who earn more than all employees working in department 30:
select ∗ from EMP where SAL > all (select SAL from EMP where DEPTNO = 30) and DEPTNO <> 30;
For all and any, the following equivalences hold: in ⇔ = anynot in ⇔ <> all or != all Often a query result depends on whether certain rows do (not) exist in (other) tables. Such type of queries is formulated using the exists operator.
Example: List all departments that have no employees:
select ∗ from DEPT where not exists (select ∗ from EMP where DEPTNO = DEPT.DEPTNO);
Explanation: For each tuple from the table DEPT, the condition is checked whether there exists a tuple in the table EMP that has the same department number (DEPT.DEPTNO). In case no such tuple exists, the condition is satisﬁed for the tuple under consideration and it is selected. If there exists a corresponding tuple in the table EMP, the tuple is not selected.
1.5.3 Operations on Result Sets
Sometimes it is useful to combine query results from two or more queries into a single result. SQL supports three set operators which have the pattern: <query 1><set operator><query 2> The set operators are: • union [all] returns a table consisting of all rows either appearing in the result of <query 1> or in the result of <query 2>. Duplicates are automatically eliminated unless the clause all is used. • intersect returns all rows that appear in both results <query 1> and <query 2>. • minus returns those rows that appear in the result of <query 1> but not in the result of <query 2>.
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Example: Assume that we have a table EMP2 that has the same structure and columns as the table EMP: • All employee numbers and names from both tables: select EMPNO, ENAME from EMP union select EMPNO, ENAME from EMP2; • Employees who are listed in both EMP and EMP2: select ∗ from EMP intersect select ∗ from EMP2; • Employees who are only listed in EMP: select ∗ from EMP minus select ∗ from EMP2; Each operator requires that both tables have the same data types for the columns to which the operator is applied.
1.5.4 Grouping
In Section 1.2.4 we have seen how aggregate functions can be used to compute a single value for a column. Often applications require grouping rows that have certain properties and then applying an aggregate function on one column for each group separately. For this, SQL pro- vides the clause group by <group column(s)>. This clause appears after the where clause and must refer to columns of tables listed in the from clause.
select <column(s)> from <table(s)> where <condition> group by <group column(s)> [having <group condition(s)>];
Those rows retrieved by the selected clause that have the same value(s) for <group column(s)> are grouped. Aggregations speciﬁed in the select clause are then applied to each group sepa- rately. It is important that only those columns that appear in the <group column(s)> clause can be listed without an aggregate function in the select clause !
Example: For each department, we want to retrieve the minimum and maximum salary.
select DEPTNO, min(SAL), max(SAL) from EMP group by DEPTNO;
Rows from the table EMP are grouped such that all rows in a group have the same department number. The aggregate functions are then applied to each such group. We thus get the following query result:
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DEPTNO MIN(SAL) MAX(SAL) 10 1300 5000 20 800 3000 30 950 2850
Rows to form a group can be restricted in the where clause. For example, if we add the condition where JOB = ’CLERK’, only respective rows build a group. The query then would retrieve the minimum and maximum salary of all clerks for each department. Note that is not allowed to specify any other column than DEPTNO without an aggregate function in the select clause since this is the only column listed in the group by clause (is it also easy to see that other columns would not make any sense).
Once groups have been formed, certain groups can be eliminated based on their properties, e.g., if a group contains less than three rows. This type of condition is speciﬁed using the having clause. As for the select clause also in a having clause only <group column(s)> and aggregations can be used.
Example: Retrieve the minimum and maximum salary of clerks for each department having more than three clerks. select DEPTNO, min(SAL), max(SAL) from EMP where JOB = ’CLERK’ group by DEPTNO having count(∗) > 3; Note that it is even possible to specify a subquery in a having clause. In the above query, for example, instead of the constant 3, a subquery can be speciﬁed.
A query containing a group by clause is processed in the following way: 1. Select all rows that satisfy the condition speciﬁed in the where clause. 2. From these rows form groups according to the group by clause. 3. Discard all groups that do not satisfy the condition in the having clause. 4. Apply aggregate functions to each group. 5. Retrieve values for the columns and aggregations listed in the select clause.
1.5.5 Some Comments on Tables
Accessing tables of other users
Provided that a user has the privilege to access tables of other users (see also Section 3), she/he can refer to these tables in her/his queries. Let <user> be a user in the Oracle system and <table> a table of this user. This table can be accessed by other (privileged) users using the command select ∗ from <user>.<table>;
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In case that one often refers to tables of other users, it is useful to use a synonym instead of <user>.<table>. InOracle-SQL a synonym can be created using the command create synonym <name> for <user>.<table> ; It is then possible to use simply <name> in a from clause. Synonyms can also be created for one’s own tables.
Adding Comments to Deﬁnitions
For applications that include numerous tables, it is useful to add comments on table deﬁnitions or to add comments on columns. A comment on a table can be created using the command
comment on table <table> is ’<text>’;
A comment on a column can be created using the command
comment on column <table>.<column> is ’<text>’;
Comments on tables and columns are stored in the data dictionary. They can be accessed using the data dictionary views USER TAB COMMENTS and USER COL COMMENTS (see also Section 3).
Modifying Table- and Column Deﬁnitions
It is possible to modify the structure of a table (the relation schema) even if rows have already been inserted into this table. A column can be added using the alter table command
alter table <table> add(<column><data type> [default <value>][<column constraint>]);
If more than only one column should be added at one time, respective add clauses need to be separated by colons. A table constraint can be added to a table using
alter table <table> add (<table constraint>);
Note that a column constraint is a table constraint, too. not null and primary key constraints can only be added to a table if none of the speciﬁed columns contains a null value. Table deﬁnitions can be modiﬁed in an analogous way. This is useful, e.g., when the size of strings that can be stored needs to be increased. The syntax of the command for modifying a column is alter table <table> modify(<column> [<data type>][default <value>][<column constraint>]);
Note: In earlier versions of Oracle it is not possible to delete single columns from a table deﬁnition. A workaround is to create a temporary table and to copy respective columns and rows into this new table. Furthermore, it is not possible to rename tables or columns. In the most recent version (9i), using the alter table command, it is possible to rename a table, columns, and constraints. In this version, there also exists a drop column clause as part of the alter table statement.
Deleting a Table
A table and its rows can be deleted by issuing the command drop table <table> [cascade constraints];.
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1.6 Views
In Oracle the SQL command to create a view (virtual table) has the form create [or replace] view <view-name> [(<column(s)>)] as <select-statement> [with check option [constraint <name>]];
The optional clause or replace re-creates the view if it already exists. <column(s)> names the columns of the view. If <column(s)> is not speciﬁed in the view deﬁnition, the columns of the view get the same names as the attributes listed in the select statement (if possible).
Example: The following view contains the name, job title and the annual salary of em- ployees working in the department 20: create view DEPT20 as select ENAME, JOB, SAL∗12 ANNUAL SALARY from EMP where DEPTNO = 20; In the select statement the column alias ANNUAL SALARY is speciﬁed for the expression SAL∗12 and this alias is taken by the view. An alternative formulation of the above view deﬁnition is create view DEPT20 (ENAME, JOB, ANNUAL SALARY) as select ENAME, JOB, SAL ∗ 12 from EMP where DEPTNO = 20;
A view can be used in the same way as a table, that is, rows can be retrieved from a view (also respective rows are not physically stored, but derived on basis of the select statement in the view deﬁnition), or rows can even be modiﬁed. A view is evaluated again each time it is accessed. In Oracle SQL no insert, update, ordelete modiﬁcations on views are allowed that use one of the following constructs in the view deﬁnition: • Joins • Aggregate function such as sum, min, max etc. • set-valued subqueries (in, any, all) or test for existence (exists) • group by clause or distinct clause In combination with the clause with check option any update or insertion of a row into the view is rejected if the new/modiﬁed row does not meet the view deﬁnition, i.e., these rows would not be selected based on the select statement. A with check option can be named using the constraint clause.
A view can be deleted using the command delete <view-name>.
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