1. Scalable Trigger Processing* - Eric N. Hanson et al. CSCi8701: Overview of Database Research Paper Presentation Group 4: Betsy George, Vijay Gandhi *International Conference on Data Engineering, 1999

2. Presentation Outline Motivation Problem Definition Related Work Contributions Key Concepts Validation Future Work Rewrite Today Summary

3. Motivation Traditional uses of Triggers  Constraint checking  Logging  Replication Web-based applications  Create triggers interactively E.g. Stock Ticker notification:  If share value of Google goes down below 500 notify a person Limitations of current trigger systems  Not scalable

4. Problem Definition Given: A Relational DBMS, Trigger statements, Data Stream (tokens) Find: Triggers corresponding to each token Objective: Scalable trigger processing system Constraints:  Number of distinct structures of trigger expressions is small  All distinct structures of trigger expressions should be small enough to fit in the main memory

5. Related Work Range Predicates, Marking based [Hans96b, Ston90] (large memory, complicated storage) AI [Forg82,Mira87] (smaller rule set) Parallel Processing [Gupt89,Hell98] IndexingECA Model (not scalable) The work proposed here is a combination of improvised version of some of the modules mentioned above.

6. Contribution Data Structures  If a large number of triggers are created, many of them have almost the same format  Predicate Index Structure  Most important contribution Concurrent processing  Identified 4 levels of concurrency  Implemented token-level concurrency

7. Key Concepts – TiggerMan Architecture

8. Key Concepts – Trigger Structure Example: Stock ticker notification Create trigger T1 from stock when stock.ticker = ‘GOOG’ and stock.value < 500 do notify_person(P1) Create trigger T2 from stock when stock.ticker = ‘MSFT’ and stock.value < 30 do notify_person(P2) Create trigger T3 from stock when stock.ticker = ‘ORCL’ and stock.value < 20 do notify_person(P3) Create trigger T4 from stock when stock.ticker = ‘GOOG’ do notify_person(P4)

9. Key Concepts – Expression Signature Common structures in the condition of triggers  Expression Signature: E1: stock.ticker = const1 and stock.value < const2  Expression Signature: E2: stock.ticker = const3 T4: stock.ticker = ‘GOOG’ T1: stock.ticker = ‘GOOG’ and stock.value < 500 T2: stock.ticker = ‘MSFT’ and stock.value < 30 T3: stock.ticker = ‘ORCL’ and stock.value < 20

10. Key Concepts – A-Treat Network For each trigger condition stock.ticker = const1 and stock.value < const2 Root stock.ticker = const1 stock.value < const2 alpha-node predicates Node 1Node 2

11. Key Concepts – Expression Signature Expression Signature Table Ex. ID Data Source Signature Description Constant Table Number of Constants Constant Organization E1stock…const_e12Main Memory E2stock…const_e21Main memory E1: stock.ticker = const1 and stock.value < const2 E2: stock.ticker = const3

12. Key Concepts – Constant Table Tables to include constants occurring in the condition of triggers const_e1 Ex. IDTrigger IDConstant 1Constant 2Next NodeRest E1T1GOOG500Node 2 E1T2MSFT30Node 2 E1T3ORCL20Node 2 T1: stock.ticker = ‘GOOG’ and stock.value < 500 T2: stock.ticker = ‘MSFT’ and stock.value < 30 T3: stock.ticker = ‘ORCL’ and stock.value < 20 Ex. IDTrigger IDConstant 1Next NodeRest E2T4GOOGNull Const_e2 T4: stock.ticker = ‘GOOG’

13. Key Concepts – Summary Expression Signature  Common structure in a trigger E1: stock.ticker = const1 and stock.value < const2 A-treat network  Network for trigger condition testing For a Trigger to fire, all conditions must be true Constant Tables  Constants for each Expressions Signature

14. Key Concepts - Predicate Index

15. Key Concepts - Processing Root Update Stock(ticker=GOOG,value=495) Index of stock.ticker=const1 E1 E2 const_e1const_e2 Other source Predicate index… Trigger IDConstant 1Constant 2Next Node T1GOOG500Node 2 T2MSFT30Node 2 T3ORCL20Node 2 E1: stock.ticker = const1 and stock.value < const2 const_e1

16. Concurrency  Better scalability  Even on single processor Identified elements that can be parallelized  Token-level Multiple tokens processed in parallel  Condition-level Multiple selection conditions tested concurrently  Rule-action-level Multiple rule actions fired at the same time  Data-level Set of data values in the network processed in parallel Implemented Token-level concurrency

17. Validation Important fact: If a large number of triggers are created, many of them have almost the same format Implemented as an Informix DataBlade No experimental comparisons

18. Assumptions If a large number of triggers are created, many of them have almost the same format All distinct predicate structures fit into the main memory

19. Rewrite today Validations  Provide Experimental Comparisons  Test on real datasets Examples: Execution Trace Remove sections on TriggerMan Command Language and Architecture Describe A-TREAT network

20. Summary If a large number of triggers are created, many of them have almost the same format Number of distinct signatures is small enough to fit into the main memory