1. CS 240A: Databases and Knowledge Bases Analysis of Active Databases Carlo Zaniolo Department of Computer Science University of California, Los Angeles WINTER 2002 Notes From Chapter 4 of Advanced Database Systems by Zaniolo, Ceri, Faloutsos, Snodgrass, Subrahmanian and Zicari Morgan Kaufmann, 1997

2. Properties of rule execution  Termination: For any legal transaction, the subsequent rule execution terminates.  Confluence:For any legal transaction, the subsequent rule execution terminates in the same final state.  Identical observable behavior: For any legal transaction, the subsequent rule execution is confluent and produces the same output sequence.

3. Analysis of Termination An active database has Non­Terminating Behavior iff there exists at least one transaction which produces non­terminating rule processing.  Triggering Graph (TG):  Directed graph {V, E}  Node v i  V correspond to rule r i  R  Arc  r j,r k   E means that the action of rule r j generates events which trigger rule r k  Acyclicity of the triggering graph implies the absence of non-terminating behaviors

4. Two rules creating an arc CREATE RULE T1 FOR Table1 WHEN... IF... THEN UPDATE Table1.Attr1... CREATE RULE T2 FOR Table1 WHEN UPDATED(Attr1) IF... THEN...

5. Cyclic Rules  With termination: CREATE RULE SalaryControl ON Emp WHEN INSERTED, DELETED, UPDATED (Sal) IF ( SELECT AVG (Sal) FROM Emp ) > 100 THEN UPDATE Emp SET Sal =.9 * Sal  Without termination: CREATE RULE SalaryControl2 ON Emp WHEN INSERTED, DELETED, UPDATED (Sal) IF ( SELECT AVG (Sal) FROM Emp ) > 100 THEN UPDATE Emp SET Sal = 1.1 * Sal

6. Improving Rule Analysis  Eliminate edge between two rules when:  The condition of r j is guaranteed to be false after the execution of r i.  The new data produced by r i do not satisfy the condition of r j.

7. Example of second case: Grade range rules CREATE TRIGGER CheckGradeDomain1 AFTER UPDATE OF Exam ON Grade REFERENCING NEW AS N FOR EACH ROW WHEN (N.Grade > 30) UPDATE Exam SET Grade = NULL WHERE ExamNumber = N.ExamNumber CREATE TRIGGER CheckGradeDomain2 AFTER UPDATE OF Exam ON Grade REFERENCING NEW AS N FOR EACH ROW WHEN (N.Grade < 18) THEN UPDATE Exam SET Grade = NULL WHERE ExamNumber = N.ExamNumber

8. Confluence  Confluence is the property that there only one final result (even when execution is nondeterministic)  This is often called Church-Rosser property, and the Knuth-Bendix algorithm can be used (in some cases) to determine if a given set of rules has this property  Set-oriented Rules (I.e., statement level triggering event)  Confluence is an issue when there is no total order between rules.  For tuple-oriented rules (e.g., for each row) confluence is an issue even if the rules are totally ordered.  In general confluence is hard to assure and might not be necessary.

9. Confluence (cont.)  Tuple-oriented Rules  Confluence is much harder to ensure; it requires that the final state does not depend on the system's controlled order in which tuples are accessed.  But: Confluence is not necessary or desirable in many cases.  Mutating table exception, when a table that is currently being updated also needs to be changed by a rule; may reveal lack of confluence.  Sufficient condition for confluence: Commutativity of two rules r 1 and r 2 : if for any database state, rule r i and then rule r 1 followed by r 2 produces the same database as r 2 followed by r 1.

10. Confluence: Commutative Rules create trigger T1 for C1 events modify(A1) condition C1(X), X.A1=7 actions modify(C1.A2,X,A2+1) end; create trigger T2 for C1 events modify(A1) condition C1(X), X.A1=7 actions modify(C1.A2,X,A2+2) end;

11. Observable Determinism  For every input application, rule processing execution terminates in the same final state with the same output sequence (messages, display activities, reports).  Unlike confluence, observable determinism is not necessary or desirable in many cases.  Rules may be confluent but not satisfy observable determinism: create trigger T1 for C1 events modify(A1) condition C1(X), X.A1=7 actions display(I where integer(I), I=X.A2) end; create trigger T2 for C1 events modify(A1) condition C1(X), X.A1=7 actions modify(C1.A2,X,A2+2), display(I where integer(I), I=X.A2) end;

12. Modularization Techniques for Active Rule Design  The main problem with rules: understanding their interaction.  This is not just a design issue, but rather a maintenance problem--The AI experience: XCON  Understanding rules after their design is quite hard.  Adding one rule to a rule set may lead to unexpected behaviors.  The main reason: lack of modularization devices for active rules.

13. Modularization by Stratification  Stratification: partitioning of rules into disjoint strata such that each strata includes ``uniform'' rules.  Benefit: Rule behavior can be abstracted by:  Reasoning ``locally" on each individual stratum  Reasoning ``globally" of the behavior across strata  A key design principle for providing rule modularization and control.

14. Rule Debugging and Monitoring  Purpose: run­time debugging and monitoring for tuning active rules' behavior.  Commercial systems lack of debugging and monitoring tools.  Minimum requirement: trace facility­-but even this often unavailable.  Some research prototypes offer powerful debuggers (e.g., Chimera)

15. Conclusions  Database production rules are:  Very powerful  Very widespread (on almost all relational products)  Very difficult to program—even harder to maintain  Active rule products must be enhanced:  Many products are ``implemented'', with severe limitation and sometimes ill­defined semantics  SQL99 is now published cleans up several problems --but others remain.  Concepts such as stratification will hopefully soon become a widespread notion for improving design  Declarative interfaces are possible for many active database applications integrity constraints, materialized views, virtual views, authorization, dependency control, deduction, versions,workflow management, resource management,....