This presentation on query processing and query optimization is made with many efforts. According to me, I have used the most basic/ fundamental examples and topics for the explanation.

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