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Scalable Trigger Processing* - Eric N. Hanson et al.

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. Presentation Outline. Motivation Problem Definition Related Work Contributions

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Scalable Trigger Processing* - Eric N. Hanson et al.

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  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 ECA Model (not scalable) Indexing Parallel Processing [Gupt89,Hell98] AI [Forg82,Mira87] (smaller rule set) Range Predicates, Marking based [Hans96b, Ston90] (large memory, complicated storage) 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 triggerT1fromstock whenstock.ticker = ‘GOOG’ and stock.value < 500 donotify_person(P1) • Create triggerT2fromstock whenstock.ticker = ‘MSFT’ and stock.value < 30 donotify_person(P2) • Create triggerT3fromstock whenstock.ticker = ‘ORCL’ and stock.value < 20 donotify_person(P3) • Create triggerT4 from stock whenstock.ticker = ‘GOOG’ donotify_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 T1: stock.ticker = ‘GOOG’ and stock.value < 500 T2: stock.ticker = ‘MSFT’ and stock.value < 30 T3: stock.ticker = ‘ORCL’ and stock.value < 20 T4: stock.ticker = ‘GOOG’

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

  11. Key Concepts – Expression Signature • Expression Signature Table 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 Const_e2 T1: stock.ticker = ‘GOOG’ and stock.value < 500 T2: stock.ticker = ‘MSFT’ and stock.value < 30 T3: stock.ticker = ‘ORCL’ and stock.value < 20 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 Update Stock(ticker=GOOG,value=495) Root Index of stock.ticker=const1 Other source Predicate index… E1: stock.ticker = const1 and stock.value < const2 E1 E2 const_e1 const_e2 const_e1

  16. Concurrency • 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

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