A Deductive Database with Datalog and SQL Query Languages

FernandoFernando SSááenzenz PPéérez,rez, RafaelRafael CaballeroCaballero andand YolandaYolanda GarcGarcííaa--RuizRuiz GrupoGrupo dede ProgramaciProgramacióónn DeclarativaDeclarativa (GPD)(GPD) UniversidadUniversidad ComplutenseComplutense dede MadridMadrid (Spain)(Spain)

12/5/2011 APLAS 2011 1 ~ ContentsContents 1.1. IntroductionIntroduction 2.2. QueryQuery LanguagesLanguages 3.3. IntegrityIntegrity ConstraintsConstraints 4.4. DuplicatesDuplicates 5.5. OuterOuter JoinsJoins 6.6. AggregatesAggregates 7.7. DebuggersDebuggers andand TracersTracers 8.8. SQLSQL TestTest CaseCase GeneratorGenerator 9.9. ConclusionsConclusions

12/5/2011 APLAS 2011 2 ~ 1.1. IntroductionIntroduction

 SomeSome concepts:concepts:  DatabaseDatabase (DB)(DB)  DatabaseDatabase ManagementManagement SystemSystem (DBMS)(DBMS)  DataData modelmodel  (Abstract) data structures  Operations  Constraints

12/5/2011 APLAS 2011 3 ~ IntroductionIntroduction

 DeDe--factofacto standardstandard technologiestechnologies inin databases:databases:  “Relational” model  SQL  But,But, aa currentcurrent trendtrend towardstowards deductivedeductive databases:databases:  Datalog 2.0 Conference The resurgence of Datalog inin academiaacademia andand industryindustry  Ontologies  Semantic Web  Social networks  Policy languages

12/5/2011 APLAS 2011 4 ~ Introduction.Introduction. SystemsSystems

 Classic academic deductive systems:  LDL++ (UCLA)  CORAL (Univ. of Wisconsin)  NAIL! (Stanford University)  Ongoing developments  Recent commercial deductive systems:  DLV (Italy, University of Calabria)  LogicBlox (USA)  Intellidimension (USA)  Semmle (UK)  Recent academic deductive systems:  4QL (Warsaw University)  bddbddb (Stanford University)  ConceptBase (Passau, Aachen, Tilburg Universities, since 1987)  XSB (Stony Brook University, Universidade Nova de Lisboa, XSB, Inc., Katholieke Universiteit LeuvLeuven,en, and Uppsala Universitet)  DES (Complutense University)

12/5/2011 APLAS 2011 5 ~ DatalogDatalog EducationalEducational SystemSystem (DES)(DES)

 YetYet anotheranother system,system, Why?Why?  WeWe neededneeded anan interactiveinteractive systemsystem targetedtargeted atat teachingteaching DatalogDatalog inin classroomsclassrooms  So,So, whatwhat aa wholewhole setset ofof featuresfeatures wewe werewere askingasking forfor suchsuch aa system?system?  A system oriented at teaching  User-friendly:  Installation  Usability  Multiplatform (Windows, Linux, Mac, …)  Interactive  Query languages  …

12/5/2011 APLAS 2011 6 ~ DESDES ConcreteConcrete FeaturesFeatures (1/4)(1/4) Free,Free, OpenOpen--source,source, Multiplatform,Multiplatform, PortablePortable QueryQuery languages:languages: (Extended)(Extended) DatalogDatalog (Recursive)(Recursive) SQLSQL followingfollowing ANSI/ISOANSI/ISO standardstandard StratifiedStratified NegationNegation IntegrityIntegrity constraintsconstraints DuplicatesDuplicates NullNull valuevalue supportsupport àà lala SQLSQL OuterOuter joinsjoins forfor bothboth SQLSQL andand DatalogDatalog AggregatesAggregates 12/5/2011 APLAS 2011 7 ~ DESDES ConcreteConcrete FeaturesFeatures (2/4)(2/4) DeclarativeDeclarative debuggersdebuggers andand tracerstracers TestTest casecase generatorgenerator forfor SQLSQL viewsviews FullFull--fledgedfledged arithmeticarithmetic DatabaseDatabase updatesupdates TemporaryTemporary DatalogDatalog viewsviews TypeType systemsystem BatchBatch processingprocessing TextualTextual APIAPI

12/5/2011 APLAS 2011 8 ~ DESDES ConcreteConcrete FeaturesFeatures (3/4)(3/4)  ProgramProgram analysis:analysis:  Safe rules (classical safety for range restriction)  Negation  Head variables  Safe metapredicates  Aggregates  Duplicate elimination  SourceSource--toto--sourcesource programprogram transformations:transformations:  Safety (command /safe on)  Aggregates (solving)  Performance (/simplification on)  ...

12/5/2011 APLAS 2011 9 ~ DESDES ConcreteConcrete FeaturesFeatures (4/4)(4/4)

But,But, quitequite relevantrelevant featuresfeatures are:are: InteractivenessInteractiveness Database updates A wide set of commands (>70) EasyEasy toto installinstall andand useuse des.sourceforge.net RobustRobust (up(up toto bugs)bugs)

12/5/2011 APLAS 2011 10 ~ DESDES allowsallows

 TeachTeach (Declarative)(Declarative) QueryQuery Languages:Languages: FromFrom SQLSQL toto DatalogDatalog  ButBut alsoalso forfor rapidrapid prototyping:prototyping:  NovelNovel features:features:  SQL hypothetical queries  Outer joins in Datalog  Datalog and SQL declarative (algorithmic) debuggers and tracers  Test case generation for SQL views  ...... andand ExperimentExperiment withwith DatalogDatalog forfor researchresearch  Theses,Theses, Papers,Papers, …… SeeSee DESDES FactsFacts atat itsits webweb pagepage

12/5/2011 APLAS 2011 11 ~ 2.2. QueryQuery Languages.Languages. DatalogDatalog

 AA databasedatabase queryquery languagelanguage stemmingstemming fromfrom PrologProlog

Prolog Datalog Predicate Relation Goal Query

 Goals are solved one answer at a time (backtracking)  Queries are solved by computing its meaning once  DeductiveDeductive database:database:  Extensional database: Facts  Intensional database: Rules. 12/5/2011 APLAS 2011 12 ~ DatalogDatalog

 DatalogDatalog differsdiffers fromfrom Prolog:Prolog:  Datalog does not allow function symbols in arguments  Facts are ground (safety)  Datalog is truly declarative:  Clause order is irrelevant  Order of literals in a body is irrelevant  No extra-logical constructors as the feared cut

12/5/2011 APLAS 2011 13 ~ DatalogDatalog SyntaxSyntax

 Program: Set of rules.  Rule:  head :- body.  ground_head.  Head: Positive atom.  Body: Conjunctions (,) and disjunctions (;) of literals  Literal: Atom, Built-in (>, <, …).  Query:  Literal with variables or constants in arguments  Body (Conjunctive queries, …)

12/5/2011 APLAS 2011 14 ~ QueryQuery Languages.Languages. SQLSQL

 Follows ISO Standard  DQL:  SELECT Expressions FROM Relations WHERE Condition  WITH RECURSIVE LocalViewDefs Statement  ASSUME LocalViewDefs IN Statement (ongoing work)  DML:  INSERT …  UPDATE …  DELETE …  DDL:  CREATE [OR REPLACE] TABLE …  CREATE [OR REPLACE] VIEW …  DROP …

12/5/2011 APLAS 2011 15 ~ DatalogDatalog andand SQLSQL inin DESDES DES

Datalog SQL

 Deductive engine (DE): Deductive Engine

 Tabling implementation Cache  Datalog programs are solved by DE ODBC In-memory  Compilation of SQL views and Prolog (MySQL, Access, SQL Server, queries to Datalog programs DB Oracle, DB2,...)  SQL queries are also solved by DE  Interoperability is allowed: SQL and Datalog do share the deductive database!  Datalog queries ↔ SQL queries  Datalog typed relations ↔ SQL tables and views

12/5/2011 APLAS 2011 16 ~

DatalogDatalog tom grace

ExampleExample jack amy tony carolI

 Facts: fred carolII father(tom,amy). father(jack,fred). carolIII father(tony,carolII). father(fred,carolIII). mother(graceI,amy). mother(amy,fred). mother(carolI,carolII).  Query: mother(carolII,carolIII). parent(X,Y)

 Rules:  Minimal model for parent: { parent(X,Y) :- father(X,Y). (tom,amy), (grace,amy), (jack,fred), parent(X,Y) :- mother(X,Y). (amy,fred), … }

12/5/2011 APLAS 2011 17 ~

tom grace RecursionRecursion jack amy tony carolI father(tom,amy). fred carolII father(jack,fred). father(tony,carolII). father(fred,carolIII). carolIII mother(graceI,amy). mother(amy,fred). DES implements a fixpoint semantics for recursive mother(carolI,carolII). Datalog, finding the least fixpoint as answer mother(carolII,carolIII). parent(X,Y) :- father(X,Y). ancestor(tom,X) parent(X,Y) :- mother(X,Y). { ancestor(X,Y) :- ancestor(tom,amy), parent(X,Y). ancestor(tom,carolIII), ancestor(X,Y) :- ancestor(tom,fred) parent(X,Z), } ancestor(Z,Y).

12/5/2011 APLAS 2011 18 ~ SQLSQL RecursionRecursion asas ofof CurrentCurrent DBMS'sDBMS's

CREATE VIEW parent(parent,child) AS SELECT * FROM father UNION SELECT * FROM mother;

CREATE OR REPLACE VIEW ancestor(ancestor,descendant) AS WITH RECURSIVE rec_ancestor(ancestor,descendant) AS SELECT * FROM parent UNION SELECT parent,descendant FROM parent,rec_ancestor WHERE parent.child=rec_ancestor.ancestor SELECT * FROM rec_ancestor;

DES-SQL> SELECT * FROM ancestor WHERE ancestor='tom';

12/5/2011 APLAS 2011 19 ~ SQLSQL SimplifiedSimplified SyntaxSyntax inin DESDES  Simply:Simply: CREATE OR REPLACE VIEW ancestor(ancestor, descendant) AS SELECT * FROM parent UNION SELECT parent, descendant FROM parent, ancestor WHERE parent.child=ancestor.ancestor;  InsteadInstead ofof resortingresorting toto WITH:WITH: CREATE OR REPLACE VIEW ancestor(ancestor,descendant) AS WITH RECURSIVE rec_ancestor(ancestor,descendant) AS SELECT * FROM parent UNION SELECT parent, descendant FROM parent, rec_ancestor WHERE parent.child=rec_ancestor.ancestor SELECT * FROM rec_ancestor;

12/5/2011 APLAS 2011 20 ~ SQLSQL HypotheticalHypothetical QueriesQueries

CREATE TABLE flight(origin string, destination string, time real)

INSERT INTO flight VALUES('lon','ny',9.0); INSERT INTO flight VALUES('mad','par',1.5); INSERT INTO flight VALUES('par','ny',10.0);

CREATE VIEW travel(origin,destination,time) AS WITH connected(origin,destination,time) AS SELECT * FROM flight UNION SELECT flight.origin, connected.destination, flight.time+connected.time FROM flight, connected WHERE flight.destination = connected.origin SELECT * FROM connected;

12/5/2011 APLAS 2011 21 ~ SQLSQL HypotheticalHypothetical QueriesQueries

DES-Datalog> SELECT * FROM travel { answer(lon,ny,9.0), answer(mad,ny,11.5), answer(mad,par,1.5), answer(par,ny,10.0) } Info: 4 tuples computed.

DES-Datalog> ASSUME SELECT 'mad','lon',2.0 IN flight(origin,destination,time) SELECT * FROM travel; { answer(lon,ny,9.0), answer(mad,lon,2.0), answer(mad,ny,11.0), answer(mad,ny,11.5), answer(mad,par,1.5), answer(par,ny,10.0) } 12/5/2011 APLAS 2011 22 ~ Info: 6 tuples computed. 3.3. IntegrityIntegrity ConstraintsConstraints  IntegrityIntegrity constraintsconstraints (IC):(IC):  Strong constraints as known in databases  Do not mix up with constraints as in CLP(D) !  UsualUsual IC:IC:  Type (domain)  Primary key  Foreign key  Existency constraints  Check constraints  NotNot soso--usualusual IC:IC:  Candidate key  Functional dependencies  User-defined integrity constraints 12/5/2011 APLAS 2011 23 ~ TypesTypes vs.vs. DomainsDomains  ImposingImposing typetype constraints:constraints:  SQL table creation  Interactive type assertions (even at the command prompt)

 SQL: CREATE TABLE s(sno INT, name VARCHAR(10));

 Datalog: :-type(s(sno:int, name:varchar(10))).

DES-Datalog> /dbschema Info: Table(s): * s(sno:number(integer),name:string(varchar(10))) Info: No views. Info: No integrity constraints. 12/5/2011 APLAS 2011 24 ~ UserUser--defineddefined IntegrityIntegrity ConstraintsConstraints

 SQL:SQL:  CHECK constraints (not supported by DES, yet)  Triggers

CREATE TABLE t(c INT CHECK (c BETWEEN 0 AND 10));

 Datalog:

DES-Datalog> :-type(t,[c:int]) DES-Datalog> :-t(X),(X<0;X>10) DES-Datalog> /assert t(11) Error: Integrity constraint violation. ic(X) :- t(X),(X < 0 ; X > 10). Offending values in database: [ic(11)]

12/5/2011 APLAS 2011 25 ~ UserUser--defineddefined IntegrityIntegrity ConstraintsConstraints

DES-Datalog> /consult paths Info: Consulting paths... edge(a,b). a edge(a,c). edge(b,a). b c edge(b,d). path(X,Y) :- path(X,Z), edge(Z,Y). path(X,Y) :- edge(X,Y). d end_of_file. Info: 6 rules consulted. DES-Datalog> :-path(X,X) Error: Integrity constraint violation. ic(X) :- path(X,X). Offending values in database: [ic(b),ic(a)] Info: Constraint has not been asserted.

12/5/2011 APLAS 2011 26 ~ 4.4. DuplicatesDuplicates

 SQLSQL isis notnot setset--oriented,oriented, ratherrather itit allowsallows duplicatesduplicates inin basebase relationsrelations andand queryquery outcomesoutcomes  So,So, forfor supportingsupporting SQLSQL asas DatalogDatalog programsprograms wewe need:need:  MultisetsMultisets  DuplicateDuplicate eliminationelimination

12/5/2011 APLAS 2011 27 ~ DuplicatesDuplicates asas ofof DESDES

 Duplicates are disabled by default DES-Datalog> /duplicates on DES-Datalog> /assert t(1) DES-Datalog> /assert t(1) DES-Datalog> t(X) { t(1), t(1) } Info: 2 tuples computed.

 Rules can also be source of duplicates, as in: DES-Datalog> /assert s(X):-t(X) DES-Datalog> s(X) { s(1), s(1) } Info: 2 tuples computed. 12/5/2011 APLAS 2011 28 ~ DuplicatesDuplicates asas ofof DESDES

 Duplicates even in recursive rules (LDL does not allow this)

DES-Datalog> /assert t(X):-t(X) DES-Datalog> t(X) { t(1), t(1), t(1), t(1) } Info: 4 tuples computed.

 No SQL implementation support this

12/5/2011 APLAS 2011 29 ~ DuplicatesDuplicates asas ofof DESDES  DiscardingDiscarding duplicatesduplicates withwith metapredicates:metapredicates:  distinct/1  distinct/2

DES-Datalog> distinct(t(X)) Info: Processing: answer(X) :- distinct(t(X)). { answer(1) } Info: 1 tuple computed.

 SQLSQL

DES-Datalog> select distinct * from t

12/5/2011 APLAS 2011 30 ~ SafetySafety andand DuplicatesDuplicates

 SetSet variablesvariables inin duplicateduplicate metapredicatesmetapredicates areare notnot boundbound

distinct([X],t(X,Y))

 Unsafe goal:

distinct([X],t(X,Y)), s(Y)

DES-Datalog> distinct([X],t(X,Y)), s(Y) Error: Incorrect use of shared set variables in metapredicate: [Y] DES-Datalog> /safe on DES-Datalog> distinct([X],t(X,Y)), s(Y) Info: Processing: answer(X,Y,C) :- s(Y), distinct([X],t(X,Y)).

12/5/2011 APLAS 2011 31 ~ 5.5. OuterOuter JoinsJoins

 NullNull values:values:  Cte.: null  Functions: is_null(Var) is_not_null(Var)  OuterOuter joinjoin builtbuilt--ins:ins:  Left ( ): lj(Left_Rel, Right_Rel, ON_Condition)  Right ( ): rj(Left_Rel, Right_Rel, ON_Condition)  Full ( ): fj(Left_Rel, Right_Rel, ON_Condition)

12/5/2011 APLAS 2011 32 ~ OuterOuter JoinJoin ExamplesExamples

 SQL: SELECT * FROM a LEFT JOIN b ON x=y;  Datalog: lj(a(X), b(Y), X=Y)

 SQL: SELECT * FROM a LEFT JOIN b WHERE x=y;  Datalog: lj(a(X), b(X), true)

 SQL: SELECT * FROM a LEFT JOIN (b RIGHT JOIN c ON y=u) ON x=y;  Datalog: lj(a(X), rj(b(Y), c(U,V), Y=U), X=Y)

12/5/2011 APLAS 2011 33 ~ 6.6. AggregatesAggregates

 DESDES offersoffers twotwo possibilities:possibilities:  AA 'group'group by'by' metapredicatemetapredicate withwith expressionsexpressions includingincluding aggregateaggregate functionsfunctions  AggregateAggregate predicatespredicates withwith groupinggrouping criteriacriteria atat thethe rulerule headhead

12/5/2011 APLAS 2011 34 ~ AggregatesAggregates

 AggregateAggregate functions:functions:  countcount –– COUNT(*)COUNT(*)  count(V)count(V) –– COUNT(C)COUNT(C)  min(V)min(V) -- MIN(C)MIN(C)  max(V)max(V) -- MAX(C)MAX(C)  sum(V)sum(V) -- SUM(C)SUM(C)  avg(Var)avg(Var) -- AVG(C)AVG(C)  times(Var)times(Var) -- NoNo SQLSQL counterpartcounterpart

12/5/2011 APLAS 2011 35 ~ AggregatesAggregates

 MetapredicateMetapredicate group_by/3

group_by( Relation, % FROM / WHERE [Var_1,…,Var_n], % GroupingGrouping columnscolumns Condition) % HAVING / ProjectionProjection

12/5/2011 APLAS 2011 36 ~ AggregatesAggregates ExampleExample employee  Number of employees for each department: Name Department Salary

DES-Datalog> group_by(employee(N,D,S), anderson accounting 1200 [D], andrews accounting 1200

R=count). arlingon accounting 1000 Info: Processing: answer(D,R) :- nolan null null group_by(employee(N,D,S),[D],R=count). norton null null

{ randall resources 800 answer(accounting,3), answer(null,2), sanders sales null answer(resources,1), silver sales 1000

answer(sales,5) smith sales 1000 } Info: 4 tuples computed. Steel sales 1020 Sullivan sales null

12/5/2011 APLAS 2011 37 ~ AggregatesAggregates ExampleExample (contd.)(contd.) employee  Active employees of departments with Name Department Salary more than one active employee: anderson accounting 1200

DES-Datalog> group_by(employee(N,D,S), andrews accounting 1200 [D], arlingon accounting 1000 count(S)>1). Info: Processing: nolan null null answer(D) :- norton null null group_by(employee(N,D,S), randall resources 800 [D], (A = count(S), A > 1)). sanders sales null { silver sales 1000 answer(accounting), smith sales 1000 answer(sales) } Steel sales 1020 Info: 2 tuples computed. Sullivan sales null

12/5/2011 APLAS 2011 38 ~ AggregatesAggregates

 AggregateAggregate metapredicates:metapredicates:  count(Relation,Result)  count(Relation,CountedVar,Result)  min(Relation,Result)  max(Rel,Result)  sum(Rel,SummedVar,Result)  avg(Rel,AvgdVar,Result)  times(Rel,MultdVar,Var)

DES-Datalog> count(employee(_,_,_),C) Info: Processing: answer(C) :- count(employee(_,_,_),[],C). 12/5/2011 APLAS 2011 39 ~ AggregateAggregate PredicatesPredicates andand GroupGroup ByBy

 Number of employees for each department.  Recall predicate group_by and functions:

DES-Datalog> group_by(employee(N,D,S), [D], C=count)

 With aggregate predicates:

DES-Datalog> c(D,C) :- count(employee(N,D,S),S,C) { c(accounting,3), c(null,0), c(resources,1), c(sales,3) }

12/5/2011 APLAS 2011 40 ~ AggregatesAggregates andand RecursionRecursion

% SQL Program % Datalog Program

CREATE OR REPLACE VIEW path(X,Y,1) :- shortest_paths(Origin,Destination,Length) AS edge(X,Y). WITH RECURSIVE path(X,Y,L) :- path(Origin,Destination,Length) AS path(X,Z,L0), (SELECT edge.*,1 FROM edge) edge(Z,Y), UNION count(edge(A,B),Max), (SELECT L0

% SQL Query % Datalog Query: SELECT * FROM shortest_paths; shortest_paths(X,Y,L) :- min(path(X,Y,Z),Z,L).

12/5/2011 APLAS 2011 41 ~ SafetySafety andand AggregatesAggregates

 SetSet variablesvariables inin aggregateaggregate metapredicatesmetapredicates areare notnot boundbound

group_by(t(X,Y),[X],C=count)

 Unsafe goal:

group_by(t(X,Y),[X],C=count), s(Y)

DES-Datalog> group_by(t(X,Y),[X],C=count), s(Y) Error: Incorrect use of shared set variables in metapredicate: [Y] DES-Datalog> /safe on DES-Datalog> group_by(t(X,Y),[X],C=count), s(Y) Info: Processing: answer(X,Y,C) :- s(Y), group_by(t(X,Y),[X],C = count).

12/5/2011 APLAS 2011 42 ~ AggregatesAggregates andand DISTINCTDISTINCT

 ForFor discardingdiscarding duplicatesduplicates (functions(functions andand metapredicates):metapredicates):  sum_distinct  count_distinct - SELECT DISTINCT COUNT(*)  count_distinct(V) - SELECT COUNT(DISTINCT C)  avg_distinct  times_distinct No need for min_distinct and max_distinct

12/5/2011 APLAS 2011 43 ~ 7.7. DeguggersDeguggers andand Tracers.Tracers. DatalogDatalog DeclarativeDeclarative DebuggerDebugger

 Motivation:Motivation:  AbstractAbstract thethe solvingsolving--orientedoriented debuggingdebugging procedureprocedure  Roots:Roots:  [Shaphiro83],[Shaphiro83], AlgorithmicAlgorithmic ProgramProgram DebuggingDebugging  SemanticsSemantics--orientedoriented

12/5/2011 APLAS 2011 44 ~ DeclarativeDeclarative DebuggerDebugger

between(X,Z):- br(X),br(Y),br(Z),X

odd(Y) :- even(Z), next(Z,Y). Elements in an odd position

br_is_even :- even(X), not(next(X,Y)). Succeeds if the cardinality is even br(a). Base relation (sequence of elements) br(b).

12/5/2011 APLAS 2011 45 ~ DeclarativeDeclarative Debugging:Debugging: SemanticSemantic GraphGraph

br_is_even = { }

next(nil, Y) = {next(nil,b)} even(X) = {even(nil)}

br(nil) = { } odd(X) = {odd(b)} Non-valid

br(Y) = {br(a), br(b)} next(b, X) = { } Unknown

has_preceding(a)= {has_preceding(a)} br(b) = {br(b)}

br(a)= {br(a)} has_preceding(b)= {}

12/5/2011 APLAS 2011 46 ~ DeclarativeDeclarative Debugging:Debugging: AA PracticalPractical SessionSession

DES> /debug_datalog br_is_even

Debugger started ... Is br(b) = {br(b)} valid(v)/non-valid(n) [v]? v Is has_preceding(b) = {} valid(v)/non-valid(n) [v]? n Is br(X) = {br(b),br(a)} valid(v)/non-valid(n) [v]? v ! Error in relation: has_preceding/1 ! Witness query: has_preceding(b) = { }

12/5/2011 APLAS 2011 47 ~ SQLSQL DebuggerDebugger

 MotivationMotivation asas ofof DatalogDatalog DebuggerDebugger  AddsAdds traversingtraversing strategiesstrategies  DivideDivide && QueryQuery /debug_sql Guest order(dq)  Also,Also, trustedtrusted tablestables /debug_sql Guest trust_tables(no)  AndAnd trustedtrusted specifications:specifications: /debug_sql Guest trust_file(pets_trust)

12/5/2011 APLAS 2011 48 ~ SQLSQL DebuggerDebugger

Owner( id integer primary key, DES-SQL> /debug_sql Guest name varchar(50)); Info: Outcome of view 'LessThan6': Pet( code integer primary key, { name varchar(50), specie 'LessThan6'(1), varchar(20)); 'LessThan6'(1), 'LessThan6'(2), PetOwner( id integer, code 'LessThan6'(2), integer, primary key (id,code), 'LessThan6'(3), Info: Outcome of view 'AnimalOwner': foreign key (id) references 'LessThan6'(4) { Owner(id), foreign key (code) } references Pet(code)) AnimalOwner(1,'Kitty',cat), Input: Is this view valid? (y/n/a) [y]: y AnimalOwner(1,'Wilma',dog), Info: Outcome of view AnimalOwner(2,'Lucky',dog), AnimalOwner 'NoCommonName':'NoCommonName': AnimalOwner(2,'Wilma',cat), LessThan6 (AnimalOwner) { AnimalOwner(3,'Oreo',cat), CatsAndDogsOwner 'NoCommonName'(1), (AnimalOwner) AnimalOwner(3,'Rocky',dog), (AnimalOwner) 'NoCommonName'(2), NoCommonName AnimalOwner(4,'Cecile',turtle), NoCommonName 'NoCommonName'(3) (CatsAndDogsOwner) AnimalOwner(4,'Chelsea',dog) } Guest (NoCommonName, } LessThan6 ) Input: Is this view valid? (y/n/a) [y]: n LessThan6 ) Input: Is this view valid? (y/n/a) [y]: y Info: Outcome of view DES-SQL> select * from Guest 'CatsAndDogsOwner':'CatsAndDogsOwner': Info: Buggy view found: CatsAndDogsOwner/2. answer(Guest.id, Guest.name) -> { { 'CatsAndDogsOwner'(1,'Wilma'), answer(1,'Mark Costas'), 'CatsAndDogsOwner'(2,'Lucky'), answer(2,'Helen Kaye'), 'CatsAndDogsOwner'(3,'Rocky') answer(3,'Robin Scott') } } Input: Is this view valid? (y/n/a) [y]: n Info: 3 tuples computed.

12/5/2011 APLAS 2011 49 ~ DatalogDatalog TracerTracer a

a :- not(b). b c b :- c,d. c :- b. c. d

DES-Datalog> /c negation Info : Remaining predicates: DES-Datalog> /trace_datalog a [c/0,d/0] Info: Tracing predicate 'a'. Input: Continue? (y/n) [y]: { Info: Tracing predicate 'c'. a { } c Info: 1 tuple in the answer table. } Info : Remaining predicates: Info: 1 tuple in the answer table. [b/0,c/0,d/0] Info : Remaining predicates: [d/0] Input: Continue? (y/n) [y]: Input: Continue? (y/n) [y]: Info: Tracing predicate 'b'. Info: Tracing predicate 'd'. { { not(b) } } Info: No more predicates to trace. Info: 1 tuple in the answer table.

12/5/2011 APLAS 2011 50 ~ SQLSQL TracerTracer DES-SQL> /trace_sql ancestor Info: Tracing view 'ancestor'. { Info : Remaining views: [mother/2] ancestor(amy,carolIII), ... Input: Continue? (y/n) [y]: ancestor(tony,carolIII) Info: Tracing view 'mother'. } { Info: 16 tuples in the answer table. { Info : Remaining views: mother(amy,fred), ... [parent/2,father/2,mother/2] mother(grace,amy) Input: Continue? (y/n) [y]: } Info: Tracing view 'parent'. Info: 4 tuples in the answer table. { Info: No more views to trace. parent(amy,fred), ... DES-SQL> /trace_datalog father(X,Y) parent(tony,carolII) } Info: Tracing predicate 'father'. Info: 8 tuples in the answer table. { Info : Remaining views: [father/2,mother/2] father(fred,carolIII), ... Input: Continue? (y/n) [y]: father(tony,carolII) Info: Tracing view 'father'. } { Info: 4 tuples in the answer table. father(fred,carolIII), ... Info: No more predicates to trace. father(tony,carolII) } Info: 4 tuples in the answer table. 12/5/2011 APLAS 2011 51 ~ 8.8. SQLSQL TestTest CaseCase GeneratorGenerator

 ProvidesProvides tuplestuples thatthat cancan bebe matchedmatched toto thethe intendedintended interpretationinterpretation ofof aa viewview  TestTest casescases  Positive (PTC)  Negative (NTC)  QueryingQuerying aa viewview w.r.t.w.r.t.  PTC: One tuple, at least  NTC: One tuple, at least, which does not match the WHERE condition  PredicatePredicate coverage:coverage:  PNTC: Contains both PTC and NTC tuples

12/5/2011 APLAS 2011 52 ~ SQLSQL TestTest CaseCase GeneratorGenerator

 PNTCPNTC DES-SQL> create table t(a int primary key) DES-SQL> create view v(a) as select a from t where a=5 DES-SQL> /test_case v Info: Test case over integers: [t(5),t(-5)]  NoNo PNTCPNTC create view v(a) as select a from t where a=1 and not exists (select a from t where a<>1);  SupportSupport for:for:  Integer and string types  Aggregates, UNION  Options:  Adding/replacing results to a table  Kind of generated test case (PTC, NTC, PNTC)  Test case size 12/5/2011 APLAS 2011 53 ~ 9.9. ConclusionsConclusions

 SuccessfulSuccessful implementationimplementation guidedguided byby needneed  WidelyWidely used,used, bothboth forfor teachingteaching andand researchresearch  More than 35,000 downloads  Up to more than 1,500 downloads/month  IncludesIncludes novelnovel featuresfeatures  Hypothetical SQL  Declarative debuggers  Outer joins  ButBut keykey factorsfactors areare also:also:  Datalog and SQL integration  Interactive, user-friendly, multiplatform system  Just download it and play! 12/5/2011 APLAS 2011 54 ~ LimitationsLimitations (Future(Future Work)Work)

 DataData areare constants,constants, nono termsterms (functions)(functions) areare allowedallowed  DatalogDatalog databasedatabase updatesupdates  BeyondBeyond 2.5VL2.5VL  SQLSQL coveragecoverage stillstill incompleteincomplete  PrecisePrecise syntaxsyntax errorerror reportsreports  ConstraintsConstraints ((àà lala CLP)CLP)  PerformancePerformance  …… onlyonly toto namename aa few!few!

12/5/2011 APLAS 2011 55 ~ Canada  University of California, at Los Angeles  Efficient Integrity Checking for Databases with Recursive CS240A: Databases and Knowledge Bases Views Davide Martinenghi and Henning Christiansen  The University of Arizona In Advances in Databases and Information Systems: 9th CsC372 East European Conference, ADBIS 2005, Tallinn, Estonia,  The State University of New York September 12-15, 2005 : Proceedings Autor Johann Eder, Hele-Mai Haav, Ahto DESDESKalja, Jaan FactsFactsUniversity at Buffalo Penjam CSE 636: Data Integration ISBN 3540285857, 9783540285854  The University of British Columbia  PhD CS304: Introduction to Relational Databases Computer Science and Engineering Department University of Nebraska - Lincoln, USA Datalog Tutorial  PhD  Master's of Information Technology in University of Texas at San Antonio, USA Arkansas Tech University, Russellville Industry:  The University of Texas at Austin  XLOG Technologies GmbH, Zürich CS2  CaseLab : Applied Operations Research  Ideacube Australia: Links to DES:  INFO2820: Database Systems 1 (Advanced) (2010 -  ACM SIGMOD Online Publicly Available Database Semester 1) Software from Nonprofit Organizations Engineering and Information Technologies  The ALP Newsletter. vol. 21 n. 1 The University of Sydney  Datalog Wikipedia German Tab “Resources”  Datalog Wikipedia English  INFO2120/2820: Database Systems 1 (2009 - Semester 1)  Wapedia School of Information Technologies  SWI-Prolog. Related Web Resources The University of Sydney  SICStus Prolog. Third Party Software. Other Research Systems Tutorial 3  SOFTPEDIA. Datalog Educational System 1.7.0  Allan Hancock College >> INFO >> 2120 Fall, 2009  Famouswhy Description: School of Information Technologies  DBpedia INFO2120/2820: Database Systems I 1.Sem./2009  BDD-Based Deductive DataBase (bddbddb) Tutorial 3: SQL and Relational Algebra 23.03.2009 Other implementations of Datalog/Prolog  Reach Information Africa:  Ask a Word  Faculty of Sciences and Technologies of  Acronym finder Mohammedia (FSTM) - Morocco 12/5/2011Acronym Geek APLAS 2011 56 ~  .