Adbms 40 heuristics in query optimization

Advance Database Management Systems : 40
Heuristics in Query Optimization
Prof Neeraj Bhargava
Vaibhav Khanna
Department of Computer Science
School of Engineering and Systems Sciences
Maharshi Dayanand Saraswati University Ajmer

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7. Using Heuristics in Query Optimization(1)
• Process for heuristics optimization
1. The parser of a high-level query generates an initial internal
representation;
2. Apply heuristics rules to optimize the internal representation.
3. A query execution plan is generated to execute groups of
operations based on the access paths available on the files
involved in the query.
• The main heuristic is to apply first the operations that reduce
the size of intermediate results.
– E.g., Apply SELECT and PROJECT operations before applying
the JOIN or other binary operations.

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Using Heuristics in Query Optimization (2)
• Query tree:
– A tree data structure that corresponds to a relational algebra
expression. It represents the input relations of the query as leaf
nodes of the tree, and represents the relational algebra
operations as internal nodes.
• An execution of the query tree consists of executing an
internal node operation whenever its operands are available
and then replacing that internal node by the relation that
results from executing the operation.
• Query graph:
– A graph data structure that corresponds to a relational calculus
expression. It does not indicate an order on which operations to
perform first. There is only a single graph corresponding to each
query.

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• 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.
• Relation algebra:
PNUMBER, DNUM, LNAME, ADDRESS, BDATE (((PLOCATION=‘STAFFORD’(PROJECT))
DNUM=DNUMBER (DEPARTMENT)) MGRSSN=SSN (EMPLOYEE))
• SQL query:
Q2: 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’;

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• Heuristic Optimization of Query Trees:
– The same query could correspond to many different relational
algebra expressions — and hence many different query trees.
– The task of heuristic optimization of query trees is to find a final
query tree that is efficient to execute.
• Example:
Q: SELECT LNAME
FROM EMPLOYEE, WORKS_ON, PROJECT
WHERE PNAME = ‘AQUARIUS’ AND
PNMUBER=PNO AND ESSN=SSN
AND BDATE > ‘1957-12-31’;

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• General Transformation Rules for Relational Algebra Operations:
1. Cascade of : A conjunctive selection condition can be broken up into a
cascade (sequence) of individual  operations:
–  c1 AND c2 AND ... AND cn(R) = c1 (c2 (...(cn(R))...) )
2. Commutativity of : The  operation is commutative:
– c1 (c2(R)) = c2 (c1(R))
3. Cascade of : In a cascade (sequence) of  operations, all but the last one
can be ignored:
– List1 (List2 (...(Listn(R))...) ) = List1(R)
4. Commuting  with : If the selection condition c involves only the
attributes A1, ..., An in the projection list, the two operations can be
commuted:
– A1, A2, ..., An (c (R)) = c (A1, A2, ..., An (R))

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• General Transformation Rules for Relational Algebra Operations (contd.):
5. Commutativity of ( and x ): The operation is commutative as is the x
operation:
– R C S = S C R; R x S = S x R
6. Commuting  with (or x ): If all the attributes in the selection condition c
involve only the attributes of one of the relations being joined—say, R—
the two operations can be commuted as follows:
– c ( R S ) = (c (R)) S
• Alternatively, if the selection condition c can be written as (c1 and c2),
where condition c1 involves only the attributes of R and condition c2
involves only the attributes of S, the operations commute as follows:
– c ( R S ) = (c1 (R)) (c2 (S))

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• General Transformation Rules for Relational Algebra
Operations (contd.):
7. Commuting  with (or x): Suppose that the projection list is
L = {A1, ..., An, B1, ..., Bm}, where A1, ..., An are attributes of R
and B1, ..., Bm are attributes of S. If the join condition c
involves only attributes in L, the two operations can be
commuted as follows:
– L ( R C S ) = (A1, ..., An (R)) C ( B1, ..., Bm (S))
• If the join condition C contains additional attributes not in L,
these must be added to the projection list, and a final 
operation is needed.

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8. Commutativity of set operations: The set operations υ and ∩
are commutative but “–” is not.
9. Associativity of , x, υ, and ∩ : These four operations are
individually associative; that is, if q stands for any one of these
four operations (throughout the expression), we have
– ( R q S ) q T = R q ( S q T )
10. Commuting  with set operations: The  operation
commutes with υ , ∩ , and –. If q stands for any one of these
three operations, we have
– c ( R q S ) = (c (R)) q (c (S))

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• The  operation commutes with υ.
L ( R υ S ) = (L (R)) υ (L (S))
• Converting a (, x) sequence into : If the condition c of a 
that follows a x Corresponds to a join condition, convert the
(, x) sequence into a as follows:
(C (R x S)) = (R C S)
• Other transformations

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• Outline of a Heuristic Algebraic Optimization Algorithm:
1. Using rule 1, break up any select operations with conjunctive conditions into a
cascade of select operations.
2. Using rules 2, 4, 6, and 10 concerning the commutativity of select with other
operations, move each select operation as far down the query tree as is permitted
by the attributes involved in the select condition.
3. Using rule 9 concerning associativity of binary operations, rearrange the leaf
nodes of the tree so that the leaf node relations with the most restrictive select
operations are executed first in the query tree representation.
4. Using Rule 12, combine a Cartesian product operation with a subsequent select
operation in the tree into a join operation.
5. Using rules 3, 4, 7, and 11 concerning the cascading of project and the commuting
of project with other operations, break down and move lists of projection
attributes down the tree as far as possible by creating new project operations as
needed.
6. Identify subtrees that represent groups of operations that can be executed by a
single algorithm.

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• Summary of Heuristics for Algebraic Optimization:
1. The main heuristic is to apply first the operations that reduce
the size of intermediate results.
2. Perform select operations as early as possible to reduce the
number of tuples and perform project operations as early as
possible to reduce the number of attributes. (This is done by
moving select and project operations as far down the tree as
possible.)
3. The select and join operations that are most restrictive should
be executed before other similar operations. (This is done by
reordering the leaf nodes of the tree among themselves and
adjusting the rest of the tree appropriately.)

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• Query Execution Plans
– An execution plan for a relational algebra query consists of a
combination of the relational algebra query tree and
information about the access methods to be used for each
relation as well as the methods to be used in computing the
relational operators stored in the tree.
– Materialized evaluation: the result of an operation is stored as
a temporary relation.
– Pipelined evaluation: as the result of an operator is produced,
it is forwarded to the next operator in sequence.

Assignment
• Explain the concept and usage of Heuristics in
Query Optimization

Adbms 40 heuristics in query optimization

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Adbms 40 heuristics in query optimization