Chapter 18 Query Processing and Optimization

1. Chapter 18 Query Processing and Optimization

2. Chapter Outline • Introduction . • Using Heuristics in Query Optimization • Query Trees and Query Graphs • Transformation Rules for RA expressions • Heuristic Optimization

3. Introduction • With declarative languages such as SQL, user specifies what data is required rather than how it is to be retrieved. • Relieves user of knowing what constitutes good execution strategy. • Also gives DBMS more control over system performance. • Two main techniques for query optimization: • heuristic rules that order operations in a query. • comparing different strategies based on relative costs, and selecting one that minimizes resource usage.

4. Introduction Query Processing (QP) : Activities involved in retrieving data from the database. • Aims of QP: • transform query written in high-level language (e.g. SQL), into correct and efficient execution strategy expressed in low-level language (implementing RA); • execute the strategy to retrieve required data. Query Optimization (QO): Activity of choosing an efficient execution strategy for processing query. • As there are many equivalent transformations of same high-level query, aim of QO is to choose one that minimizes resource usage. • Generally, reduce total execution time of query. • May also reduce response time of query. • Problem computationally intractable with large number of relations, so strategy adopted is reduced to finding near optimum solution.

5. Introduction

6. Query Trees and Query Graphs

7. Transformation Rules forRA Operations • Cascade of Selection: Conjunctive selection operations can cascade into individual selection operations spq r(R) = sp (sq ( sr (R))) • Commutativity of selection. sp (sq (R)) = sq ( sp(R)) • Cascade of Projection:In a sequence of projection operations, only the last in the sequence is required. L M … N (R) = L (R) • Commutativity of s and  : Ifthe selection condition c involves only those attributes A1, … An in the projection list, then the two operations can commute: Ai, …, Am (sc (R)) = sc (Ai, …, Am(R))

8. Transformation Rules forRA Operations • Commutativity of  (and  ). R p S = S p R R  S = S  R • Associativity of  (and ).  and natural join (*)are always associative: (R * S)  T = R * (S * T) (R  S)  T = R  (S  T) If join condition q involves attributes only from S and T, then  is associative as follows: (R p S) q  r T = R p  r (S q T)

9. Transformation Rules forRA Operations • Commutativity of s and  (or ). If all the attributes in the selection condition c involves only the attributes of one of the relations being joined – Say R – then the two operations can be commuted as follows: sc (R r S) = (sc (R)) r S sc (R  S) = (sc (R))  S Alternatively if the condition c can be written as (c1 and c2), where condition c1 involves only the attributes of the relations R and condition c2 involves only the attributes of the relations S then the two operations can be commuted as follows: sc1 (R r S) = (sc1 (R)) r (sc2 ( S)) sc1 (R  S) = (sc1 (R))  (sc2 ( S))

10. Transformation Rules for RA Operations • Commutativity of  and  (or ). If projection list is of form L = L1 L2, where L1 only involves attributes of R, and L2 only involves attributes of S, provided join condition only contains attributes of L, projection and theta-join operations commute as: L1  L2 (R r S) = (L1(R)) r (L2(S)) If join condition contains additional attributes not in L, say attributes M = M1 M2 where M1 only involves attributes of R, and M2 only involves attributes of S, a final projection operation is required: L1  L2(RrS) =L1  L2( (L1 M1(R)) r(L2 M2(S))) • Commutativity of  and  (but not - ). R  S = S  R R  S = S  R • Associativity of  and  (but not - ). (R  S)  T = S  (R  T) (R  S)  T = S  (R  T)

11. Transformation Rules for RA Operations Commutativity of s and set operations (, , and -). sp(R  S) = sp(S) sp(R) sp(R  S) = sp(S) sp(R) sp(R - S) = sp(S) -sp(R) • Commutativity of  and . L (R  S) = L(S) L(R)

12. Heuristical Processing Strategies • Perform selection operations as early as possible. • Keep predicates on same relation together. • Combine Cartesian product with subsequent selection whose predicate represents join condition into a join operation. • Use associativity of binary operations to rearrange leaf nodes so leaf nodes with most restrictive selection operations executed first. • Perform projection as early as possible. • Keep projection attributes on same relation together. • Compute common expressions once. • If common expression appears more than once, and result not too large, store result and reuse it when required. • Useful when querying views, as same expression is used to construct view each time.

13. Use of Transformation Rules Q: Find the last name of employees born after 1975 who work on a project named ‘Aquarius’. SELECT LNAME, FROM EMPLOYEE, WORKS_ON, PROJECT, WHERE PNAME=‘Aquarius’ AND ESSN= SSN AND PNUMBER=PNO AND BDATE > ‘1957-12-31’

14. Use of Transformation Rules

15. Example 18.3 Use of Transformation Rules