This document discusses query optimization techniques, specifically heuristic query optimization. It outlines that query optimization aims to choose the most efficient execution strategy to minimize resource usage. Typical optimization steps include parsing, optimization, evaluation, and execution. Heuristic optimization uses logical and physical optimization techniques like rearranging select operations to be more restrictive first, converting cartesian products followed by selection into joins, and moving project operations down the query tree. An example is provided of translating an SQL query into a relational algebra query tree and applying heuristic optimization techniques like join reordering to produce a more efficient query execution plan. Cost-based optimization also considers factors like access costs, storage costs, and communication costs to determine the lowest cost plan.

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Query Optimization Using
Heuristic Approach
e-Infochips Institute of Training Research and Academics Limited
Prepared By:
Monika Sanghani

2. Query Optimization
Query optimization
Techniques
Translating SQL into
Relational Algebra Operations
Heuristic examples
Summary
Outlines

3. What is Query Optimization and Why
• The activity of choosing an efficient execution strategy for
processing a query.
• An important aspect of query processing is query optimization.
• The aim of query optimization is to choose the one that
minimizes resource usage.
• The part of the DBMS responsible for selecting an execution
strategy is the query optimizer, and it passes the selected
strategy to the query-code generator which creates the code
and sends it off to be processed.

4. Typical Steps In Processing
1.Parsing & translation
2.Optimization
3.Evaluation
4. Execution

5. Heuristic query optimization Cost-based query optimization
-Logical optimization
-Oracle calls this Rule Based
optimization.
Two Ways:
• Query tree (relational algebra)
optimization
• Query graph optimization
-Physical optimization
-Several Cost components to
be consider:
• Access cost
• Storage Cost
• Computation costs
• Communications Costs
Query optimization Techniques

6. Query block: The basic unit that can be translated into the algebraic operators and
optimized.
A query block contains a single SELECT-FROM-WHERE expression, as well as
GROUP BY and HAVING clause if these are part of the block.
Nested queries within a query are identified as separate query blocks.
Translating SQL Queries into Relational Algebra

7. Simple Example of Query Block

8. Translating SQL into Relational Algebra

9. Query tree
Tree that represents a relational algebra expression.
• Leaves = base tables.
• Internal nodes = relational algebra operators
applied to the node’s children.
• The tree is executed from leaves to root.

10. 1. Break up conjunctive select into cascade
2. Move down select as far as possible in the tree
3. Rearrange select operations: The most restrictive(ones that produce a relation
with the fewest tuples or with the smallest absolute size.) should be executed first
4. Convert Cartesian product followed by selection into join
5. Move down project operations as far as possible in the tree. Create new
projections so that only the required attributes are involved in the tree
6. Identify sub trees that can be executed by a single algorithm
Heuristic Algorithm

11. Structure of the Initial Query Tree

12. Heuristic Query Tree Optimization
Example :
SELECT LNAME
FROM EMPLOYEE, WORKS_ON, PROJECT
WHERE PNAME= ‘AQUARIUS’ AND PNUMBER=PNO AND ESSN=SSN AND BDATE > ‘1957-12-31’
A. Initial Query tree

13. Analysis of the Initial Query Tree
(Set operations : Unions, Intersections, Complements, Cartesian product, etc.
Aggregate functions : Average, Count, Max, Min, Sum, etc.)

14. B. Moving SELECT operations down the query tree

15. Question for You

16. C. Applying the more restrictive SELECT operation first

17. D. Replacing CARTESIAN PRODUCT and SELECT with JOIN operations

18. E. Moving PROJECT operations down the query tree

19. Effect of Project Operations

20. Query Graph
Query graph: a graph data structure that does not indicate an order on which
operations to perform first. There is only a single graph corresponding to each query.
• Nodes represents Relations.
• Ovals represents constant nodes.
• Edges represents Join & Selection conditions.
• Attributes to be retrieved from relations represented in square
brackets.

21. Example :
For every project located in ‘Stafford’, retrieve the project number, the
controlling department number and the department manager’s last name, address
and birthdate.
SQL query:
SELECT P.NUMBER,P.DNUM,E.LNAME, E.ADDRESS, E.BDATE
FROM PROJECT AS P,DEPARTMENT AS D, EMPLOYEE AS E
WHERE P.DNUM=D.DNUMBER AND D.MGRSSN=E.SSN AND
P.PLOCATION=‘STAFFORD’;

22. Cost Based Optimization

23. How Optimization Make Efficient Database

25. Summary (heuristic optimization)

26. Thank you