This presentation is made with many efforts and I believe that it will be proven as good presentation to clear the basic of query processing and optimization under the DBMS subject. The topics covered in this presentation are the basic fundamentals of the topic as suggested.

1. QUERY PROCESSING AND
QUERY OPTIMIZATION
By NIRAJ GANDHA

2. What is Query Processing?
 It is a 3 step process that transforms a high level query
(sql) into an equivalent and more efficient lower-level
query (of relational algebra).

3. Query
Query
 Query is the statement written by the user in high language
using pl/sql.

4. Parser & Translator
Query
Parser
&
Translator
 Parser: Checks the syntax and verifies the relation.
 Translator: Translates the query into an equivalent
relational algebra.
Example:
SQL> select name from customer;
RA:=∏name(customer)

5. Relational Algebra
Query
Parser
&
Translator
Relational
Algebra
 It is the query converted in algebraic form from pl/ sql by
translator.
 Example:
SQL>SELECT ENAME FROM EMP,ASG WHERE
EMP.ENO=ASG.ENO AND DUR>37;
RA:1) ΠENAME(σDUR>37∧EMP.ENO=ASG.ENO(EMP × ASG))
2) ΠENAME(EMP ENO (σDUR>37(ASG)))

6. Optimizer
Query
Parser
&
Translator
Relational
Algebra
Optimizer
 It will select the query which has low cost.
Example:
1) ΠENAME(σDUR>37∧EMP.ENO=ASG.ENO(EMP × ASG))
2) ΠENAME(EMP ENO (σDUR>37(ASG)))
Optimizer will select Expression2 as it avoids
the expensive and large intermediate
Cartesian product, and therefore typically is
better.

7. Comparison of two relational queries
 ΠENAME(σDUR>37∧EMP.ENO=ASG.ENO
(EMP × ASG))
 ΠENAME(EMP ENO(σDUR>37(ASG)))
EMP x ASG
Temp as
EMP.ENO=ASG.ENO
ΠENAME
ENO(σDUR>37(ASG)
EMP ENO
ΠENAME
σDUR>37 ∧temp

8. Query
Parser
&
Translator
Relational
Algebra
Optimizer
Statistical
Data
Statistical Data
 A Statical Data is a
database used for
statistical analysis
purposes.
 It is an OLAP(Online
Analytical Processing),
instead of OLTP(Online
Transaction Processing)
system

9. Evaluation Plan
Query
Parser
&
Translator
Relational
Algebra
Optimizer
Statistical
Data
Evaluation
Plan
 Relational Algebra
annotated with instructions
on how to evaluate it is
called an evaluation
primitive.
 Sequence of primitive
operations that can be
used to evaluate a query is
a query evaluation plan.

10. EVALUATION & DATA
 The evaluation
engine takes
the evaluation
plan as
condition and
applies it on
the data.
Query
Parser
&
Translator
Relational
Algebra
Optimizer
Statistical
Data
Evaluation
Plan
Evaluation
 The information on
which the query has
to be performed is
called data.Data

11. OUTPUT
Query
Parser
&
Translator
Relational
Algebra
Optimizer
Statistical
Data
Evaluation
Plan
EvaluationOutput
Data
 After the evaluation of
plan on data,
processed information
is showed in output.

12. Diagram of Query Processing
Query
Parser
&
Translator
Relational
Algebra
Optimizer
Statistical
Data
Evaluation
Plan
EvaluationOutput
Data

13. Measures of Query Cost
 The cost of query evaluation can be measured in
terms of different resources, including
 disk accesses
 CPU time to execute a query in a distributed or
parallel database system
 the cost of communication.

14. Materialization
 In materialization approach, output of every single operation
is saved in temporary relation for the subsequent use.
 It starts from the lowest-level operations in the expression.
 Ex: Πcustomer(σbalance<2500(account) customer)
Πcustomer
σbalance<2500 customer
account

15. Pipelining
 In pipelining approach, output of every single operation is not
necessary to save in temporary relation for the subsequent
use.
 In this the operations take place simultaneously or in
background
 It starts from the lowest-level operations in the expression.
 Ex: Πcustomer(σbalance<2500(account) customer)
Πcustomer
σbalance<2500 customer
account

16. Query Optimization
 It is the process of selecting the most efficient query-
evaluation plan from among the many strategies usually
possible for processing a given query, especially if the query is
complex.

17. Example of Optimization
 ∏customer(σbranch_city=”Brooklyn”(branch
(account depositor)))
∏customer
σbranch_city=”Brooklyn”
branch
account depositor
 ∏customer((σbranch_city=”Brooklyn”(branc
h)) (account depositor))
∏customer
σbranch_city=”Brooklyn”
branch account depositor

18. The end