1. Query Compiler

2. The Query Compiler Parses SQL query into parse tree Transforms parse tree into expression tree (logical query plan) Transforms logical query plan into physical query plan

3. {P1,P2,…..} {P1,C1>...} parse convert apply laws estimate result sizes consider physical plans estimate costs pick best execute Pi answer SQL query parse tree logical query plan “improved” l.q.p l.q.p. +sizes statistics

4. Grammar for simple SQL ::= ::= ( ) ::= SELECT FROM WHERE ::=, ::= ::=, ::= ::= AND ::= IN ::= = ::= LIKE ::= Atoms(constants), (variable), ::= (can be expressed/defined as)

5. Query StarsIn(title,year,starName) MovieStar(name,address,gender,birthdate) Query: Give titles of movies that have at least one star born in 1960 SELECT title FROM StarsIn, MovieStar WHERE starName = name AND birthdate LIKE '%1960%' ;

6. Parse Tree SELECT FROM WHERE, AND title StarsIn = LIKE starName name birthdate ‘%1960’ MovieStar

7. Another query equivalent SELECT title FROM StarsIn WHERE starName IN ( SELECT name FROM MovieStar WHERE birthdate LIKE '%1960%' );

8. The Preprocessor (expand query & semantic checking) Checks against schema definition: –Relation uses –Attribute uses, resolve names ( A to R.A) –Use of types (strings, integers, dates, etc) and operators’ arguments type/arity  These preprocessing functions are called semantic checking If all tests are passed, then the parse tree is said to be valid

9. Algebraic laws for transforming logical query plans Commutative and associative laws: Above laws are applicable for both sets and bags

10. Theta-join Commutative: Not always associative: –On schema R(a,b), S(b,c), T(c,d) the first query can not be transformed into the second: (Why?) Because, we can’t join S and T using the condition a<d since a is an attribute of neither S nor T.

11. Laws Involving Selection (  ) Splitting laws Only if R is a set. The union is “set union” Order is flexible

12. Laws Involving Selection (  ) What about intersection?

13. Algebraic Laws involving selection For the binary operators, we push the selection only if all attributes in the condition C are in R.

14. Example: Consider relation schemas R(A,B) and S(B,C) and the expression below:  (A=1 OR A=3) AND B<C (R  S) 1.Splitting AND  A=1 OR A=3 (  B < C (R  S)) 2.Push  to S  A=1 OR A=3 (R   B < C (S)) 3.Push  to R  A=1 OR A=3 (R)   B < C (S)

15. Pushing selections Usually selections are pushed down the expression tree. The following example shows that it is sometimes useful to pull selection up in the tree. StarsIn(title,year,starName) Movie(title,year,length,studioName) CREATE VIEW MoviesOf1996 AS SELECT * FROM MOVIE WHERE year=1996; Query: Which stars worked for which studios in 1996? SELECT starName,studioName FROM MoviesOf1996 NATURAL JOIN StarsIN;

16. pull selection up then push down

17. Laws for (bag) Projection A simple law: Project out attributes that are not needed later. –i.e. keep only the input attr. and any join attribute.

18. Examples for pushing projection Schema R(a,b,c), S(c,d,e)

19. Example: Pushing Projection Schema : StarsIn(title,year,starName) Query: SELECT starName FROM StarsIn WHERE year = 1996; Should we transform to  ? Depends! Is StarsIn stored or computed ?  starName  year=1996 StarsIn  starName  year=1996 StarsIn  starName,year

20. Reasons for not pushing the projection If StarsIn is stored, then for the projection we have to scan the relation. If the relation is pipelined from some previous computation, then yes, we better do the projection (on the fly). Also, if for example there is an index on year for StarsIn, such index is useless in the projected relation  starName,year (StarsIn) –While such an index is very useful for the selection on “ year=1996 ”

21. Laws for duplicate elimination and grouping Try to move  in a position where it can be eliminated altogether E.g. when  is applied on A stored relation with a declared primary key A relation that is the result of a  operation, since grouping creates a relation with no duplicates.  absorbs 

22. Improving logical query plans Push  as far down as possible (sometimes pull them up first). Do splitting of complex conditions in  in order to push  even further. Push  as far down as possible, introduce new early  (but take care for exceptions) Combine  with  to produce  - joins or equi-joins Choose an order for joins

23. Example of improvement SELECT title FROM StarsIn, MovieStar WHERE starName = name AND birthdate LIKE ‘%1960’;  title  starname=name AND birthdate LIKE ‘%1960’ StarsIn  MovieStar  title StarsIn MovieStar starName=name  birthdate LIKE ‘%1960’

24.  title StarsIn MovieStar starName=name  birthdate LIKE ‘%1960’  name And a better plan introducing a projection to filter out useless attributes: