Query Execution Time calculation and Query Optimization....

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Query Execution Time Calculation and
Query Optimization

2. OBJECTIVE
The objective of this presentation is
to show how to calculate the time
required during the execution of a
query and then optimize the query.
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3. 1. Query Execution Time
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What is Query?
Query is piece of code that is send to a Database in order to get
information back from database. It is used to interact with Databse.
What is Query ExecutionTime?
Query ExecutionTime means time required by query to get the
required information from the database.

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HowTo Calculate ??? ?
Execution of any query involve CPU time and time to access data from
disk. CPU speed are increasing much faster than the disk access time. CPU time
as it depends on the low level details of execution code so it is harder to calculate.
We have used System time in order to get Execution time.
As the System time represent the computer system's notion of the
passing of time.
System time is measured by a system clock, which is typically
implemented as a simple count of the number of ticks
Implementation:-
The system clock is typically implemented as a programmable interval
timer that periodically interrupts the CPU, which then starts executing a timer
interrupt service routine.That routine typically adds one tick to the system clock
(a simple counter) and handles other periodic housekeeping tasks before
returning to whatever the CPU was doing before the interruption.

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 Screen Shots:-
 Advantage:-
If we get the execution time then we can reduce that time by using some
query optimization method.
It also Help in Query Scheduling, Progress Monitoring and also control to
System size.

6. 2. Query Optimization
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 Introduction
 Steps in Cost-based query optimization- Query Flow
 Transformation of Relational Expressions
 Optimization Algorithms
 Future Scope
 Conclusion

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Query Optimization
 What is Query Optimization??? ?
Query optimization is a function of many relational database management
systems.
The query optimizer attempts to determine the most efficient way to
execute a given query by considering the possible query plans.
 Why ?
◦ Many different ways of executing a given query.
◦ Huge differences in Execution Time.
◦ Increase performance of the system.
◦ Uses Less Memory.
◦ Lesser Stress on the Database.

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Query Optimization
 Example:
◦ select * from person where ssn = “123”
◦ Size of person = 1MB
◦ Sequential Scan:
 Takes 1MB / (20mb/s) = 50ms
◦ Use an index on SSN (assuming one exists):
 Approx 4 Random I/Os = 40ms

9. Query Optimization
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 Introduction
 Steps in Time-based query optimization- Query Flow
 Transformation of Relational Expressions
 Optimization Algorithms
 Future Scope
 Conclusion

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Steps inTime Based Query Optimization:-
1. Parsing.
2. Transformation.
3. Implementation.
4. Plan selection based onTime estimates.
Query Flow:-
Processor
Code Generator
Optimizer
Parser
SQL
Query Language
Relational Calculus
Relational and Physical Algebra
Record at a time calls

11.  SQL– Structured query Language. It is used to take or any type of
data from database or to Interact with database.
 Query Parser –Verify validity of the SQL statement.Translate
query into an internal structure using relational calculus.
 Query Optimizer – Find the best expression from various
different algebraic expressions.
 Code Generator/Interpreter -It takes the physical and
relational algebra as a input and gave it to the query processor.
 Query Processor – Execute the calls obtained from the code
generator.
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Steps inTime Based Query Optimization:-

12. Query Optimization
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 Introduction
 Steps in Time-based query optimization- Query Flow
 Transformation of Relational Expressions
 Optimization Algorithms
 Future Scope
 Conclusion

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Equivalence of Expressions
 Two relational expressions equivalent iff:
◦ Their result is identical on all legal databases
 Equivalence rules:
◦ Allow replacing one expression with another
 Examples:
1.
2. Selections are commutative
))(()( 2121
EE   
))(())(( 1221
EE   

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 Pictorial representation of Some more equivalence Rule :-

15. Transformation to Relational Algebra
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 Simple Algorithm
 Start with the original expression
 Apply all possible applicable rules to get a new set of
expressions
 Repeat with this new set of expressions
 Till no new expressions are generated

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 Find the names of all customers with an account at a Brooklyn branch
whose account balance is over $1000.
customer_name(branch_city = “Brooklyn”  balance > 1000
(branch (account depositor)))
 Apply the rules one by one
customer_name((branch_city = “Brooklyn”  balance > 1000
(branch account)) depositor)
customer_name(((branch_city = “Brooklyn” (branch)) ( balance > 1000
(account))) depositor)
Example

17. Query Optimization
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 Introduction
 Steps in Time-based query optimization- Query Flow
 Transformation of Relational Expressions
 Optimization Algorithms
 Future Scope
 Conclusion

18. Optimization Algorithms
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 Two Types:
◦ Exhaustive:
That attempt to find the best plan and have dynamic
programming.
◦ Heuristic:
That are simpler, but are not guaranteed to find the optimal
plan.

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Heuristic Optimization
 Dynamic programming is expensive.
 Use heuristics to reduce the number of choices.
 Typically rule-based:
◦ Perform selection early (reduces the number of tuples)
◦ Perform projection early (reduces the number of attributes)
◦ Perform most restrictive selection and join operations before other
similar operations.
 Some systems use only heuristics, others combine heuristics with
partial time-based optimization.

20. Steps inTypical Heuristic Optimization
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1. Deconstruct conjunctive selections into a sequence of single
selection operations .
2. Move selection operations down the query tree for the earliest
possible execution .
3. Execute first those selection and join operations that will produce
the smallest relations.
4. Replace Cartesian product operations that are followed by a
selection condition by join operations.
5. Deconstruct and move as far down the tree as possible lists of
projection attributes, creating new projections where needed.
6. Identify those subtrees whose operations can be pipelined, and
execute them using pipelining).

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Advantages of Query Optimization
1. Faster Processing of Query.
2. Lesser Cost Per Query.
3. High Performance of The System.
4. Lesser Stress on Database.
5. Lesser Memory is Consumed.

22. Query Optimization
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 Introduction.
 Steps in Time-based query optimization- Query Flow.
 Transformation of Relational Expressions.
 Optimization Algorithms.

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Thanks