1. Abstract State Machines and Computationally Complete Query Languages Andreas Blass,U Michigan Yuri Gurevich,Microsoft Research & U Michigan Jan Van den Bussche,U Limburg

2. Outline Databases and queries Query languages: –while new, while new sets –ASMs Notions of polynomial time Comparisons

3. Database schema = Finite set  of relation names with associated arities Database B over  = Finite structure over  –Finite domain D of atomic values –For each R  , a k-ary relation R B on D Relational databases

4. Database schema = Finite set  of relation names with associated arities Database B over  = Finite structure over  –Finite domain D of atomic values –For each R  , a k-ary relation R B on D Relational databases arity associated to R in 

5. Database schema = Finite set  of relation names with associated arities Database B over  = Finite structure over  –Finite domain D of atomic values –For each R  , a k-ary relation R B on D E.g. Graph: Relational databases arity associated to R in 

6. Database schema = Finite set  of relation names with associated arities Database B over  = Finite structure over  –Finite domain D of atomic values –For each R  , a k-ary relation R B on D E.g. Graph: Relational databases arity associated to R in  12 3 4

7. Database schema = Finite set  of relation names with associated arities Database B over  = Finite structure over  –Finite domain D of atomic values –For each R  , a k-ary relation R B on D E.g. Graph: Relational databases arity associated to R in  12 3 4 D1234D1234 E (1,2) (2,3) (2,4) (3,4)

8. Queries General definition of query: a (partial, computable) mapping Q –from databases –to relations (of a common arity) (over a common schema)

9. Queries General definition of query: a (partial, computable) mapping Q –from databases –to relations Q(B) is the answer to the query Q on database B. (of a common arity) (over a common schema)

10. Queries General definition of query: a (partial, computable) mapping Q –from databases –to relations Q(B) is the answer to the query Q on database B. Arity 0: {( )} or { }  Boolean query (of a common arity) (over a common schema)

11. Queries General definition of query: a (partial, computable) mapping Q –from databases –to relations Q(B) is the answer to the query Q on database B. Arity 0: {( )} or { }  Boolean query E.g. On a graph: –Give all pairs of nodes that are targets of a common source. (of a common arity) (over a common schema)

12. Queries General definition of query: a (partial, computable) mapping Q –from databases –to relations Q(B) is the answer to the query Q on database B. Arity 0: {( )} or { }  Boolean query E.g. On a graph: –Give all pairs of nodes that are targets of a common source. –Is f(m)=2000? (of a common arity) (over a common schema)

13. Queries General definition of query: a (partial, computable) mapping Q –from databases –to relations Q(B) is the answer to the query Q on database B. Arity 0: {( )} or { }  Boolean query E.g. On a graph: –Give all pairs of nodes that are targets of a common source. –Is f(m)=2000? (of a common arity) (over a common schema) number of edges in graph

14. Queries General definition of query: a (partial, computable) mapping Q –from databases –to relations Q(B) is the answer to the query Q on database B. Arity 0: {( )} or { }  Boolean query E.g. On a graph: –Give all pairs of nodes that are targets of a common source. –Is f(m)=2000? (of a common arity) (over a common schema) arbitrary computable function on N

15. The consistency criterion The answer of a query on a database can depend only on information that is logically contained in that database.  If h is an isomorphism B  B, then h is also an isomorphism Q(B)  Q(B).

16. Query languages In practice: SQL  first-order logic + counting, summation, … E.g. Give all pairs of nodes that are targets of a common source:

17. Query languages In practice: SQL  first-order logic + counting, summation, … E.g. Give all pairs of nodes that are targets of a common source: select E1.target, E2.target from E E1, E E2 where E1.source = E2.source

18. Query languages In practice: SQL  first-order logic + counting, summation, … E.g. Give all pairs of nodes that are targets of a common source: select E1.target, E2.target from E E1, E E2 where E1.source = E2.source  (x,y)  z ( E(z,x)  E(z,y) ) 

19. Expressiveness of first-order logic (FO) Many useful queries are expressible in FO. But many others are not: –Connectivity: Is the graph connected? –Is f(m)=2000, where if m is even if m is odd 2000 0 f(m) =

20. Expressiveness of first-order logic (FO) Many useful queries are expressible in FO. But many others are not: –Connectivity: Is the graph connected? –Is f(m)=2000, where (parity query) if m is even if m is odd 2000 0 f(m) =

21. Towards a complete language: while Make FO basis of a small programming language for working with relations: –relation variables (typed by fixed arities) –operations on relations provided by FO –assignment: X   (x 1,…,x j )  (x 1,…,x j ) 

22. Towards a complete language: while Make FO basis of a small programming language for working with relations: –relation variables (typed by fixed arities) –operations on relations provided by FO –assignment: X   (x 1,…,x j )  (x 1,…,x j )  relation variable of arity j

23. Towards a complete language: while Make FO basis of a small programming language for working with relations: –relation variables (typed by fixed arities) –operations on relations provided by FO –assignment: X   (x 1,…,x j )  (x 1,…,x j )  FO-formula over db relations and relation variables

24. Towards a complete language: while Make FO basis of a small programming language for working with relations: –relation variables (typed by fixed arities) –operations on relations provided by FO –assignment: X   (x 1,…,x j )  (x 1,…,x j )  –sequential composition

25. Towards a complete language: while Make FO basis of a small programming language for working with relations: –relation variables (typed by fixed arities) –operations on relations provided by FO –assignment: X   (x 1,…,x j )  (x 1,…,x j )  –sequential composition –while-loops: while  do … od

26. Towards a complete language: while Make FO basis of a small programming language for working with relations: –relation variables (typed by fixed arities) –operations on relations provided by FO –assignment: X   (x 1,…,x j )  (x 1,…,x j )  –sequential composition –while-loops: while  do … od FO-sentence

27. Towards a complete language: while Make FO basis of a small programming language for working with relations: –relation variables (typed by fixed arities) –operations on relations provided by FO –assignment: X   (x 1,…,x j )  (x 1,…,x j )  –sequential composition –while-loops: while  do … od Chandra & Harel [1982]

28. Example while-program Connectivity query: Seen (2)   ; Path (2)  E; while Path  Seen   do Seen  Path; Path  Path   (x,z)  y ( Path(x,y)  E(y,z) )  ; od.

29. Example while-program Connectivity query: Seen (2)   ; Path (2)  E; while Path  Seen   do Seen  Path; Path  Path   (x,z)  y ( Path(x,y)  E(y,z) )  ; od. Parity query:

30. Example while-program Connectivity query: Seen (2)   ; Path (2)  E; while Path  Seen   do Seen  Path; Path  Path   (x,z)  y ( Path(x,y)  E(y,z) )  ; od. Parity query: Not!

31. A complete language: while new S. Abiteboul & V. Vianu [1988] Allow introduction of new domain elements in the computation. New operator:

32. A complete language: while new S. Abiteboul & V. Vianu [1988] Allow introduction of new domain elements in the computation. New operator: new

33. A complete language: while new S. Abiteboul & V. Vianu [1988] Allow introduction of new domain elements in the computation. New operator: new X (3)  new R (2) abcdfgabcdfg R abcdfgabcdfg X

34. A complete language: while new S. Abiteboul & V. Vianu [1988] Allow introduction of new domain elements in the computation. New operator: new X (3)  new R (2) Every partial computable query can be programmed in while new. abcdfgabcdfg R abcdfgabcdfg X

35. Parity in while new 1Easy to check parity of a set S equipped with a successor relation: Even (0)  true; Visit (1)   first element of S  ; while Visit   do Even   Even; Visit   succ(x)  Visit(x)  od.

36. Parity in while new 2Make a set S of new elements, one for each edge: S 0  new E; S   3 (S 0 );

37. Parity in while new 3Compute a successor relation on S: Impossible!

38. Parity in while new 3Compute the tree T of all m! successor relations, where m = |S|: T  new   ; Seen   ; Extend   r,x  T  r   S  x  ; while Extend   do X  new Extend; T  T   3  X  ; succ  succ   1,3  X  ; Seen  Seen   n,x  n  X  n,x,n    x  x  Seen  n,x     ; Extend   n,x  n   3  X   S  x    Seen(n,x  od.

39. We can’t do better! while new -PSPACE: class of while new -programs running in polynomial space. Theorem: [Abiteboul–Vianu 1991] The parity query cannot be done in while new -PSPACE. Intuition: In while new you cannot make arbitrary choices (recall consistency criterion)  Instead of choosing one successor relation, we must work with them all. while new -PTIME: class of while new -PSPACE- programs running in polynomial time.

40. BGS Blass, Gurevich, Shelah [1996]: –How can we formalize algorithms that never have to make arbitrary choices? –What can such algorithms still do in polynomial time?  Instantiation of ASMs for expressing database queries.

41. BGS ASMs Universe: HF(D) –every x  D is in HF(D); –every finite set of elements of HF(D) is itself in HF(D). Infinite, but at any point only finitely many sets are “active”. Set-theoretic static functions: –  pairing –bounded set-construction forall do (parallel ASMs)

42. Connectivity with a BGS-ASM if Mode   then forall x  D do Frontier  x    x  enddo, Mode   endif, if Mode  then forall x  D do Reached  x  := Reached  x   Frontier  x  Frontier  x  :=  y  D  z  Frontier  x   E  z,y   y  Reached  x   Frontier  x  enddo, Halt   Frontier  x  x  D   endif.

43. BGS-PTIME BGS-PTIME: class of BGS-ASMs –running for at most polynomially many steps –constructing at most polynomially many sets “Choiceless polynomial time”

44. BGS-PTIME versus while new -PTIME? Structure I n :   n nn

45. BGS-PTIME versus while new -PTIME? Structure I n : There is a PTIME BGS-program that outputs:   n nn

46. BGS-PTIME versus while new -PTIME? Structure I n : There is a PTIME BGS-program that outputs: –trueon every I n with neven;   n nn

47. BGS-PTIME versus while new -PTIME? Structure I n : There is a PTIME BGS-program that outputs: –trueon every I n with neven; –falseodd.   n nn

48. BGS-PTIME versus while new -PTIME? Structure I n : There is a PTIME BGS-program that outputs: –trueon every I n with neven; –falseodd. (Just construct all red subsets of even size.)   n nn

49. BGS-PTIME versus while new -PTIME? Structure I n : There is a PTIME BGS-program that outputs: –trueon every I n with neven; –falseodd. (Just construct all red subsets of even size.) Theorem: There is no such PSPACE while new - program (let alone PTIME).   n nn

50. Sets versus lists BGS programs can construct sets. while new programs can only construct lists. –operator new works tuple- (  list-) based. Lists are ordered; sets can be unordered. If you want to simulate something unordered by something ordered, you have to work with all orders. –(Recall parity in while new.)  BGS-PTIME strictly encompasses while new -PTIME.

51. The language while new sets Theory of object-based query languages, studied late 80s – early 90s. Operator new from while new is really tuple-new.  We need also a set-new!  Language while new sets

52. set-new Y   set-new R  adaebebdcecfcgadaebebdcecfcg R

53. set-new Y   set-new R  adaebebdcecfcgadaebebdcecfcg R

54. set-new Y   set-new R  adaebebdcecfcgadaebebdcecfcg R

55. set-new Y   set-new R  adaebebdcecfcgadaebebdcecfcg R a b c a b c Y

56. Equivalence results while new sets and BGS can simulate each other. Simulation: –linear step overhead –polynomial space overhead  BGS-PTIME  while new sets -PTIME  while new -PTIME

57. Concluding remarks ASMs and query languages are quite related, and share the common concern of computation on the “logical” level. Purely mathematically, –basic parallel ASMs –while new are essentially the same thing. ASMs clearly win from query languages in flexibility, appeal to practitioners, developed philosophy, and people like Yuri and Egon. while new never “escaped” database theory! Challenge: the Web (querying XML, WWW, …)