Home | | Database Management Systems | | FUNDAMENTALS OF Database Systems | | Database Management Systems | Unary Relational Operations: SELECT and PROJECT

Chapter: Fundamentals of Database Systems - The Relational Data Model and SQL - The Relational Algebra and Relational Calculus

| Study Material, Lecturing Notes, Assignment, Reference, Wiki description explanation, brief detail |

Unary Relational Operations: SELECT and PROJECT

1. The SELECT Operation 2. The PROJECT Operation 3. Sequences of Operations and the RENAME Operation

Unary Relational Operations: SELECT and PROJECT

 

1. The SELECT Operation

 

The SELECT operation is used to choose a subset of the tuples from a relation that satisfies a selection condition.3 One can consider the SELECT operation to be a filter that keeps only those tuples that satisfy a qualifying condition. Alternatively, we can consider the SELECT operation to restrict the tuples in a relation to only those tuples that satisfy the condition. The SELECT operation can also be visualized as a horizontal partition of the relation into two sets of tuples—those tuples that sat-isfy the condition and are selected, and those tuples that do not satisfy the condition and are discarded. For example, to select the EMPLOYEE tuples whose department is 4, or those whose salary is greater than $30,000, we can individually specify each of these two conditions with a SELECT operation as follows:

 

σDno=4(EMPLOYEE) σSalary>30000(EMPLOYEE)

In general, the SELECT operation is denoted by

σ<selection condition>(R)

where the symbol σ (sigma) is used to denote the SELECT operator and the selec-tion condition is a Boolean expression (condition) specified on the attributes of relation R. Notice that R is generally a relational algebra expression whose result is a relation—the simplest such expression is just the name of a database relation. The relation resulting from the SELECT operation has the same attributes as R.

 

The Boolean expression specified in <selection condition> is made up of a number of clauses of the form

 

<attribute name> <comparison op> <constant value>

 

or

 

<attribute name> <comparison op> <attribute name>

where <attribute name> is the name of an attribute of R, <comparison op> is nor-mally one of the operators {=, <, , >, , }, and <constant value> is a constant value from the attribute domain. Clauses can be connected by the standard Boolean oper-ators and, or, and not to form a general selection condition. For example, to select the tuples for all employees who either work in department 4 and make over $25,000 per year, or work in department 5 and make over $30,000, we can specify the following SELECT operation:

 

σ(Dno=4 AND Salary>25000) OR (Dno=5 AND Salary>30000)(EMPLOYEE)

The result is shown in Figure 6.1(a).

 

Notice that all the comparison operators in the set {=, <, , >, , } can apply to attributes whose domains are ordered values, such as numeric or date domains. Domains of strings of characters are also considered to be ordered based on the col-lating sequence of the characters. If the domain of an attribute is a set of unordered values, then only the comparison operators in the set {=, } can be used. An exam-ple of an unordered domain is the domain Color = { ‘red’, ‘blue’, ‘green’, ‘white’, ‘yel-low’, ...}, where no order is specified among the various colors. Some domains allow additional types of comparison operators; for example, a domain of character strings may allow the comparison operator SUBSTRING_OF.

 

In general, the result of a SELECT operation can be determined as follows. The <selection condition> is applied independently to each individual tuple t in R. This is done by substituting each occurrence of an attribute Ai in the selection condition with its value in the tuple t[Ai]. If the condition evaluates to TRUE, then tuple t is


selected. All the selected tuples appear in the result of the SELECT operation. The Boolean conditions AND, OR, and NOT have their normal interpretation, as follows:

 

              (cond1 AND cond2) is TRUE if both (cond1) and (cond2) are TRUE; other-wise, it is FALSE.

 

              (cond1 OR cond2) is TRUE if either (cond1) or (cond2) or both are TRUE; otherwise, it is FALSE.

 

              (NOT cond) is TRUE if cond is FALSE; otherwise, it is FALSE.

 

The SELECT operator is unary; that is, it is applied to a single relation. Moreover, the selection operation is applied to each tuple individually; hence, selection condi-tions cannot involve more than one tuple. The degree of the relation resulting from a SELECT operation—its number of attributes—is the same as the degree of R. The number of tuples in the resulting relation is always less than or equal to the number of tuples in R. That is, |σc (R)| |R| for any condition C. The fraction of tuples selected by a selection condition is referred to as the selectivity of the condition.

 

Notice that the SELECT operation is commutative; that is,

 

σ<cond1>(σ<cond2>(R)) = σ<cond2>(σ<cond1>(R))

 

Hence, a sequence of SELECTs can be applied in any order. In addition, we can always combine a cascade (or sequence) of SELECT operations into a single SELECT operation with a conjunctive (AND) condition; that is,

 

σ<cond1>(σ<cond2>(...(σ<condn>(R)) ...)) = σ<cond1> AND<cond2> AND...AND <condn>(R)

 

In SQL, the SELECT condition is typically specified in the WHERE clause of a query. For example, the following operation:

 

σDno=4 AND Salary>25000 (EMPLOYEE)

would correspond to the following SQL query:

 

SELECT    *

 

FROM        EMPLOYEE

 

WHERE     Dno=4 AND Salary>25000;

 

2. The PROJECT Operation

 

If we think of a relation as a table, the SELECT operation chooses some of the rows from the table while discarding other rows. The PROJECT operation, on the other hand, selects certain columns from the table and discards the other columns. If we are interested in only certain attributes of a relation, we use the PROJECT operation to project the relation over these attributes only. Therefore, the result of the PROJECT operation can be visualized as a vertical partition of the relation into two relations: one has the needed columns (attributes) and contains the result of the operation, and the other contains the discarded columns. For example, to list each employee’s first and last name and salary, we can use the PROJECT operation as follows:

 

πLname, Fname, Salary(EMPLOYEE)

The resulting relation is shown in Figure 6.1(b). The general form of the PROJECT operation is

π<attribute list>(R)

where π (pi) is the symbol used to represent the PROJECT operation, and <attribute list> is the desired sublist of attributes from the attributes of relation R. Again, notice that R is, in general, a relational algebra expression whose result is a relation, which in the simplest case is just the name of a database relation. The result of the PROJECT operation has only the attributes specified in <attribute list> in the same order as they appear in the list. Hence, its degree is equal to the number of attributes in <attribute list>.

 

If the attribute list includes only nonkey attributes of R, duplicate tuples are likely to occur. The PROJECT operation removes any duplicate tuples, so the result of the PROJECT operation is a set of distinct tuples, and hence a valid relation. This is known as duplicate elimination. For example, consider the following PROJECT operation:

 

πSex, Salary(EMPLOYEE)

 

The result is shown in Figure 6.1(c). Notice that the tuple <‘F’, 25000> appears only once in Figure 6.1(c), even though this combination of values appears twice in the EMPLOYEE relation. Duplicate elimination involves sorting or some other tech-nique to detect duplicates and thus adds more processing. If duplicates are not elim-inated, the result would be a multiset or bag of tuples rather than a set. This was not permitted in the formal relational model, but is allowed in SQL (see Section 4.3).

 

The number of tuples in a relation resulting from a PROJECT operation is always less than or equal to the number of tuples in R. If the projection list is a superkey of R—that is, it includes some key of R—the resulting relation has the same number of tuples as R. Moreover,

π<list1> (π<list2>(R)) = π<list1>(R)

 

as long as <list2> contains the attributes in <list1>; otherwise, the left-hand side is an incorrect expression. It is also noteworthy that commutativity does not hold on

PROJECT.

 

In SQL, the PROJECT attribute list is specified in the SELECT clause of a query. For example, the following operation:

 

πSex, Salary(EMPLOYEE)

 

would correspond to the following SQL query:

 

SELECT                           DISTINCT Sex, Salary

 

FROM                               EMPLOYEE

 

Notice that if we remove the keyword DISTINCT from this SQL query, then dupli-cates will not be eliminated. This option is not available in the formal relational algebra.

 

3. Sequences of Operations and the RENAME Operation

 

The relations shown in Figure 6.1 that depict operation results do not have any names. In general, for most queries, we need to apply several relational algebra operations one after the other. Either we can write the operations as a single relational algebra expression by nesting the operations, or we can apply one operation at a time and create intermediate result relations. In the latter case, we must give names to the relations that hold the intermediate results. For example, to retrieve the first name, last name, and salary of all employees who work in depart-ment number 5, we must apply a SELECT and a PROJECT operation. We can write a single relational algebra expression, also known as an in-line expression, as follows:

 

πFname, Lname, Salary(σDno=5(EMPLOYEE))

Figure 6.2(a) shows the result of this in-line relational algebra expression. Alternatively, we can explicitly show the sequence of operations, giving a name to each intermediate relation, as follows:

DEP5_EMPS ← σDno=5(EMPLOYEE)

RESULT ← πFname, Lname, Salary(DEP5_EMPS)

 

It is sometimes simpler to break down a complex sequence of operations by specifying intermediate result relations than to write a single relational algebra expression. We can also use this technique to rename the attributes in the intermediate and


result relations. This can be useful in connection with more complex operations such as UNION and JOIN, as we shall see. To rename the attributes in a relation, we simply list the new attribute names in parentheses, as in the following example:

TEMP ← σDno=5(EMPLOYEE)

R(First_name, Last_name, Salary) πFname, Lname, Salary(TEMP)

 

These two operations are illustrated in Figure 6.2(b).

 

If no renaming is applied, the names of the attributes in the resulting relation of a SELECT operation are the same as those in the original relation and in the same order. For a PROJECT operation with no renaming, the resulting relation has the same attribute names as those in the projection list and in the same order in which they appear in the list.

 

We can also define a formal RENAME operation—which can rename either the relation name or the attribute names, or both—as a unary operator. The general RENAME operation when applied to a relation R of degree n is denoted by any of the following three forms:

ρS(B1, B2, ..., Bn)(R)                or  ρS(R)  or  ρ(B1, B2, ..., Bn)(R)

 

where the symbol ρ (rho) is used to denote the RENAME operator, S is the new relation name, and B1, B2, ..., Bn are the new attribute names. The first expression renames both the relation and its attributes, the second renames the relation only, and the third renames the attributes only. If the attributes of R are (A1, A2, ..., An) in that order, then each Ai is renamed as Bi.

 

In SQL, a single query typically represents a complex relational algebra expression. Renaming in SQL is accomplished by aliasing using AS, as in the following example:

 

SELECT                           E.Fname AS First_name, E.Lname AS Last_name, E.Salary AS Salary

 

FROM                               EMPLOYEE AS E

 

WHERE                            E.Dno=5,


Study Material, Lecturing Notes, Assignment, Reference, Wiki description explanation, brief detail


Copyright © 2018-2020 BrainKart.com; All Rights Reserved. Developed by Therithal info, Chennai.