Introduction to Data Management CSE 344. Lecture 14: Relational Calculus

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1 Introduction to Data Management CSE 344 Lecture 14: Relational Calculus CSE Fall

2 Midterm Monday, November 9 th in class Content Lectures 1 through 14 Homework 1 through 4 Closed book. No computers, phones, watches, etc.! Can bring one letter-sized piece of paper with notes Can write on both sides CSE Fall

3 How to Study? Lecture slides and section materials Homework 1 through 4 Past midterms posted on website Lots of great examples! With solutions But content changes between quarters So some questions may not apply We may have some new questions not present in past Practice webquiz on gradience (opens Nov 3rd) Ignore the XML questions (XML is on the final) Not comprehensive (indexing, query evaluation) CSE Fall

4 Big Picture Query languages and data models SQL, SQL, SQL, SQL, Relational algebra Datalog Relational calculus XPath and XQuery CSE Fall

5 Friend(name1, name2) Enemy(name1, name2) Datalog Review Find Joe's friends, and Joe's friends of friends. A(x) :- Friend('Joe', x) A(x) :- Friend('Joe', z), Friend(z, x) CSE Fall

6 Friend(name1, name2) Enemy(name1, name2) More Examples Find all of Joe's friends who do not have any friends except for Joe: JoeFriends(x) :- Friend('Joe',x) NonAns(x) :- JoeFriends(x), Friend(x,y), y!= Joe A(x) :- JoeFriends(x), NOT NonAns(x) CSE Fall

7 Friend(name1, name2) Enemy(name1, name2) More Examples Find all people such that all their enemies' enemies are their friends Q: if someone doesn't have any enemies nor friends, do we want them in the answer? A: Yes! Everyone(x) :- Friend(x,y) Everyone(x) :- Friend(y,x) Everyone(x) :- Enemy(x,y) Everyone(x) :- Enemy(y,x) NonAns(x) :- Enemy(x,y),Enemy(y,z), NOT Friend(x,z) A(x) :- Everyone(x), NOT NonAns(x) CSE Fall

8 Friend(name1, name2) Enemy(name1, name2) More Examples Find all persons x that have a friend all of whose enemies are x's enemies. Everyone(x) :- Friend(x,y) NonAns(x) :- Friend(x,y) Enemy(y,z), NOT Enemy(x,z) A(x) :- Everyone(x), NOT NonAns(x) CSE Fall

9 Datalog Summary EDB (base relations) and IDB (derived relations) Datalog program = set of rules Datalog is recursive But we learn non-recursive datalog Pure datalog does not have negation; if we want negation we say datalog+negation Multiple atoms in a rule mean join (or intersection) Variables with the same name are join variables Multiple rules with same head mean union CSE Fall

10 Relational Calculus Aka predicate calculus or first order logic TRC = Tuple RC See book DRC = Domain RC We study only this one Also see: Query Language Primer CSE Fall

11 Relational Calculus Relational predicate P is a formula given by this grammar: P ::= atom P P P P P P not(p) x.p x.p Query Q: Q(x1,, xk) = P CSE Fall

12 Relational Calculus Relational predicate P is a formula given by this grammar: P ::= atom P P P P P P not(p) x.p x.p Query Q: Q(x1,, xk) = P Example: find the first/last names of actors who acted in 1940 Q(f,l) = x. y. z. (Actor(z,f,l) Casts(z,x) Movie(x,y,1940)) What does this query return? Q(f,l) = z. (Actor(z,f,l) x.(casts(z,x) CSE Fall 2015 y.movie(x,y,1940))) 12

13 Important Observation Find all bars that serve all beers that Fred likes A(x) = y. Likes("Fred", y) => Serves(x,y) Note: P => Q (read P implies Q) is the same as (not P) OR Q In this query: If Fred likes a beer the bar must serve it (P => Q) In other words: Either Fred does not like the beer (not P) OR the bar serves that beer (Q). A(x) = y. not(likes("fred", y)) OR Serves(x,y) CSE Fall

14 More Examples Average Joe Find drinkers that frequent some bar that serves some beer they like. CSE Fall

15 More Examples Average Joe Find drinkers that frequent some bar that serves some beer they like. Q(x) = y. z. Frequents(x, y) Serves(y,z) Likes(x,z) CSE Fall

16 More Examples Find drinkers that frequent some bar that serves some beer they like. Q(x) = y. z. Frequents(x, y) Serves(y,z) Likes(x,z) Prudent Peter Find drinkers that frequent only bars that serves some beer they like. Average Joe CSE Fall

17 More Examples Find drinkers that frequent some bar that serves some beer they like. Q(x) = y. z. Frequents(x, y) Serves(y,z) Likes(x,z) Find drinkers that frequent only bars that serves some beer they like. Average Joe Prudent Peter Q(x) = y. Frequents(x, y) ( z. Serves(y,z) Likes(x,z)) CSE Fall

18 More Examples Find drinkers that frequent some bar that serves some beer they like. Q(x) = y. z. Frequents(x, y) Serves(y,z) Likes(x,z) Prudent Peter Find drinkers that frequent only bars that serves some beer they like. Q(x) = y. Frequents(x, y) ( z. Serves(y,z) Likes(x,z)) Find drinkers that frequent some bar that serves only beers they like. Average Joe Cautious Carl CSE Fall

19 More Examples Find drinkers that frequent some bar that serves some beer they like. Q(x) = y. z. Frequents(x, y) Serves(y,z) Likes(x,z) Find drinkers that frequent only bars that serves some beer they like. Q(x) = y. Frequents(x, y) ( z. Serves(y,z) Likes(x,z)) Find drinkers that frequent some bar that serves only beers they like. Average Joe Prudent Peter Cautious Carl Q(x) = y. Frequents(x, y) z.(serves(y,z) Likes(x,z)) CSE Fall

20 More Examples Find drinkers that frequent some bar that serves some beer they like. Q(x) = y. z. Frequents(x, y) Serves(y,z) Likes(x,z) Find drinkers that frequent only bars that serves some beer they like. Q(x) = y. Frequents(x, y) ( z. Serves(y,z) Likes(x,z)) Find drinkers that frequent some bar that serves only beers they like. Q(x) = y. Frequents(x, y) z.(serves(y,z) Likes(x,z)) Find drinkers that frequent only bars that serves only beer they like. Average Joe Prudent Peter Cautious Carl Paranoid Paul CSE Fall

21 More Examples Find drinkers that frequent some bar that serves some beer they like. Q(x) = y. z. Frequents(x, y) Serves(y,z) Likes(x,z) Find drinkers that frequent only bars that serves some beer they like. Q(x) = y. Frequents(x, y) ( z. Serves(y,z) Likes(x,z)) Find drinkers that frequent some bar that serves only beers they like. Q(x) = y. Frequents(x, y) z.(serves(y,z) Likes(x,z)) Find drinkers that frequent only bars that serves only beer they like. Average Joe Prudent Peter Cautious Carl Paranoid Paul Q(x) = y. Frequents(x, y) z.(serves(y,z) Likes(x,z)) CSE Fall

22 Domain Independent Relational Calculus As in datalog, one can write unsafe RC queries; they are also called domain dependent A(x) = not Likes("Fred", x) A(x,y) = Likes("Fred", x) OR Serves("Bar", y) A(x) = y. Serves(x,y) Lesson: make sure your RC queries are domain independent (only depends on database) CSE Fall

23 Relational Calculus How to write a complex SQL query: Write it in RC Translate RC to datalog Translate datalog to SQL Take shortcuts when you know what you re doing CSE Fall

24 From RC to Datalog to SQL Query: Find drinkers that like some beer so much that they frequent all bars that serve it Q(x) = y. Likes(x, y) z.(serves(z,y) Frequents(x,z)) CSE Fall

25 From RC to Datalog to SQL Query: Find drinkers that like some beer so much that they frequent all bars that serve it Q(x) = y. Likes(x, y) z.(serves(z,y) Frequents(x,z)) Step 1: Replace with using de Morgan s Laws Q(x) = y. Likes(x, y) z.(serves(z,y) Frequents(x,z)) P Q same as P Q x P(x) same as x P(x) ( P Q) same as P Q CSE Fall

26 From RC to Datalog to SQL Query: Find drinkers that like some beer so much that they frequent all bars that serve it Q(x) = y. Likes(x, y) z.(serves(z,y) Frequents(x,z)) Step 1: Replace with using de Morgan s Laws Q(x) = y. Likes(x, y) z.(serves(z,y) Frequents(x,z)) P Q same as P Q x P(x) same as x P(x) ( P Q) same as P Q Step 2: Make all subqueries domain independent Q(x) = y. Likes(x, y) z.(likes(x,y) Serves(z,y) Frequents(x,z)) CSE Fall

27 From RC to Datalog to SQL Q(x) = y. Likes(x, y) z.(likes(x,y) Serves(z,y) Frequents(x,z)) H(x,y) Step 3: Create a datalog rule for each subexpression; (shortcut: only for important subexpressions) H(x,y) :- Likes(x,y),Serves(z,y), not Frequents(x,z) Q(x) :- Likes(x,y), not H(x,y) CSE Fall

28 From RC to Datalog to SQL H(x,y) :- Likes(x,y),Serves(z,y), not Frequents(x,z) Q(x) :- Likes(x,y), not H(x,y) Step 4: Write it in SQL SELECT DISTINCT L.drinker FROM Likes L WHERE CSE Fall

29 From RC to Datalog to SQL H(x,y) :- Likes(x,y),Serves(z,y), not Frequents(x,z) Q(x) :- Likes(x,y), not H(x,y) Step 4: Write it in SQL SELECT DISTINCT L.drinker FROM Likes L WHERE not exists (SELECT * FROM Likes L2, Serves S WHERE ) CSE Fall

30 From RC to Datalog to SQL H(x,y) :- Likes(x,y),Serves(z,y), not Frequents(x,z) Q(x) :- Likes(x,y), not H(x,y) Step 4: Write it in SQL SELECT DISTINCT L.drinker FROM Likes L WHERE not exists (SELECT * FROM Likes L2, Serves S WHERE L2.drinker=L.drinker and L2.beer=L.beer and L2.beer=S.beer and not exists (SELECT * FROM Frequents F WHERE F.drinker=L2.drinker and F.bar=S.bar)) CSE Fall

31 From RC to Datalog to SQL H(x,y) :- Likes(x,y),Serves(z,y), not Frequents(x,z) Q(x) :- Likes(x,y), not H(x,y) Unsafe rule Improve the SQL query by using an unsafe datalog rule SELECT DISTINCT L.drinker FROM Likes L WHERE not exists (SELECT * FROM Serves S WHERE L.beer=S.beer and not exists (SELECT * FROM Frequents F WHERE F.drinker=L.drinker and F.bar=S.bar)) CSE Fall

32 Summary: all these formalisms are equivalent! We have seen these translations: RA à datalog RC à datalog Practice at home, and read Query Language Primer: Nonrecursive datalog à RA RA à RC Summary: RA, RC, and non-recursive datalog can express the same class of queries, called Relational Queries CSE Fall