Page 217 - DCAP402_DCAO204_DATABASE MANAGEMENT SYSTEM_MANAGING DATABASE

Database Management Systems/Managing Database

Notes 12.5 Evaluation of Expression

Before we discuss the evaluation of a query expression, let us briefly explain how a SQL query
may be represented. Consider the following student and marks relations:

STUDENT (enrolno, name, phone)
MARKS (enrolno, subjectcode, grade)
To find the results of the student(s) whose phone number is ‘1129250025’, the following query
may be given.
SELECT enrolno, name, subjectcode, grade
FROM STUDENT s, MARKS m

WEHRE s.enrolno=m.enrolno AND phone= ’1129250025’
The equivalent relational algebraic query for this will be:

( (STUDENT)  MARKS)
enrolno, name, subjectcode phone=’1129250025’
This is a very good internal representation however, it may be a good idea to represent the
relational algebraic expression to a query tree on which algorithms for query optimisation can
be designed easily. In a query tree, nodes are the operators and relations represent the leaf. The
query tree for the relational expression above would be:
Figure 12.5: A Sample Query Tree

In the previous section, we have seen the algorithms for individual operations. Now let us look
at the methods for evaluating an entire expression. In general we use two methods:
1. Materialisation
2. Pipelining.

Materialisation

Evaluate a relational algebraic expression from the bottom-up, by explicitly generating and
storing the results of each operation in the expression. For example, in Figure 12.5 compute and
store the result of the selection operation on STUDENT relation, then take the join of this result
with MARKS relation and then finally compile the projection operation.
Materialised evaluation is always possible though; the cost of writing/reading results to/from
disk can be quite high.

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