1. CS 255: Database System Principles slides: From Parse Trees to Logical Query Plans By:- Arunesh Joshi Id:-006538558

2. Agenda Conversion to Relational Algebra. Removing Sub queries From Conditions. Improving the Logical Query Plan. Grouping Associative/Commutative Operators.

3. Parsing Goal is to convert a text string containing a query into a parse tree data structure: – leaves form the text string (broken into lexical elements) – internal nodes are syntactic categories Uses standard algorithmic techniques from compilers – given a grammar for the language (e.g., SQL), process the string and build the tree

4. Two steps to turn Parse tree into Preferred Logical Query Plan Replace the nodes and structures of the parse tree, in appropriate groups, by an operator or operators of relational algebra. Take the relational algebra expression and turn it into an expression that we expect can be converted to the most efficient physical query plan.

5. Reference Relations StarsIn (movieTitle, movieYear, starName) MovieStar (name, address, gender, birthdate) Conversion to Relational Algebra If we have a with a that has no subqueries, then we may replace the entire construct – the select-list, from-list, and condition – by a relational-algebra expression.

6. Example: SQL query SELECT title FROM StarsIn WHERE starName IN ( SELECT name FROM MovieStar WHERE birthdate LIKE ‘%1960’ ); (Find the movies with stars born in 1960) Assume we have a simplified grammar for SQL.

7. Example: Parse Tree SELECT FROM WHERE IN title StarsIn ( ) starName LIKE name MovieStar birthDate ‘%1960’ SELECT FROM WHERE

8. The Preprocessor It replaces each reference to a view with a parse (sub)-tree that describes the view (i.e., a query) It does semantic checking: – are relations and views mentioned in the schema? – are attributes mentioned in the current scope? – are attribute types correct?

9. Convert Parse Tree to Relational Algebra The complete algorithm depends on specific grammar, which determines forms of the parse trees Here is a flavor of the approach

10. Conversion Suppose there are no subqueries. SELECT att-list FROM rel-list WHERE cond is converted into PROJ att-list (SELECT cond (PRODUCT(rel-list))), or  att-list (  cond ( X (rel-list)))

11. SELECT movieTitle FROM StarsIn, MovieStar WHERE starName = name AND birthdate LIKE '%1960'; SELECT FROM WHERE, AND movieTitle StarsIn LIKE MovieStar birthdate '%1960' = starName name

12. Equivalent Algebraic Expression Tree  movieTitle starname = name AND birthdate LIKE '%1960' X StarsIn MovieStar 

13. Handling Subqueries Recall the (equivalent) query: SELECT title FROM StarsIn WHERE starName IN ( SELECT name FROM MovieStar WHERE birthdate LIKE ‘%1960’ ); Use an intermediate format called two- argument selection

14.  title  StarsIn IN  name  birthdate LIKE ‘%1960’ starName MovieStar Example: Two-Argument Selection

15. Converting Two-Argument Selection To continue the conversion, we need rules for replacing two-argument selection with a relational algebra expression Different rules depending on the nature of the sub query Here is shown an example for IN operator and uncorrelated query (sub query computes a relation independent of the tuple being tested)

16. Rules for IN  R t IN S R  S  X C C is the condition that equates attributes in t with corresponding attributes in S

17. Example: Logical Query Plan  title  starName=name StarsIn  name  birthdate LIKE ‘%1960’ MovieStar 

18. What if Subquery is Correlated? Example is when subquery refers to the current tuple of the outer scope that is being tested More complicated to deal with, since subquery cannot be translated in isolation Need to incorporate external attributes in the translation Some details are in textbook

19. Improving the Logical Query Plan There are numerous algebraic laws concerning relational algebra operations By applying them to a logical query plan judiciously, we can get an equivalent query plan that can be executed more efficiently Next we'll survey some of these laws

20. Example: Improved Logical Query Plan  title starName=name StarsIn  name  birthdate LIKE ‘%1960’ MovieStar

21. Associative and Commutative Operations product natural join set and bag union set and bag intersection  associative: (A op B) op C = A op (B op C)  commutative: A op B = B op A

22. Laws Involving Selection Selections usually reduce the size of the relation Usually good to do selections early, i.e., "push them down the tree" Also can be helpful to break up a complex selection into parts

23. Selection Splitting  C1 AND C2 (R) =  C1 (  C2 (R))  C1 OR C2 (R) = (  C1 (R)) U set (  C2 (R)) if R is a set  C1 (  C2 (R)) =  C2 (  C1 (R))

24. Selection and Binary Operators Must push selection to both arguments: –  C (R U S) =  C (R) U  C (S) Must push to first arg, optional for 2nd: –  C (R - S) =  C (R) - S –  C (R - S) =  C (R) -  C (S) Push to at least one arg with all attributes mentioned in C: – product, natural join, theta join, intersection – e.g.,  C (R X S) =  C (R) X S, if R has all the atts in C

25. Pushing Selection Up the Tree Suppose we have relations – StarsIn(title,year,starName) – Movie(title,year,len,inColor,studioName) and a view – CREATE VIEW MoviesOf1996 AS SELECT * FROM Movie WHERE year = 1996; and the query – SELECT starName, studioName FROM MoviesOf1996 NATURAL JOIN StarsIn;

26. The Straightforward Tree  starName,studioName  year=1996 StarsIn Movie Remember the rule  C (R S) =  C (R) S ?

27. The Improved Logical Query Plan  starName,studioName  year=1996 StarsIn Movie  starName,studioName  year=1996 Movie StarsIn  starName,studioName  year=1996 Movie StarsIn push selection up tree push selection down tree

28. Grouping Associative/ Commutative Operators An operator that is associative and commutative operators may be though of as having any number of operands. We need to reorder these operands so that the multiway join is executed as sequence of binary joins. Its more time consuming to execute them in the order suggested by parse tree. For each portion of subtree that consists of nodes with the same associative and commutative operator (natural join, union, and intersection), we group the nodes with these operators into a single node with many children.

29. Grouping Assoc/Comm Operators Groups together adjacent joins, adjacent unions, and adjacent intersections as siblings in the tree Sets up the logical QP for future optimization when physical QP is constructed: determine best order for doing a sequence of joins (or unions or intersections) U D E F U U A BC DEF A B C