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
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Motivation
Problem Definition
Related Work
Contributions
Key Concepts
Validation
Future Work
Rewrite Today
Summary
Motivation
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Traditional uses of Triggers
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Constraint checking
Logging
Replication
Web-based applications
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Create triggers interactively
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E.g. Stock Ticker notification:
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If share value of Google goes down below 500
notify a person
Limitations of current trigger systems
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Not scalable
Problem Definition
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Given: A Relational DBMS, Trigger statements, Data
Stream (tokens)
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Find: Triggers corresponding to each token
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Objective: Scalable trigger processing system
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Constraints:
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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
Related Work
ECA Model
(not scalable)
Indexing
Range Predicates, Marking based
[Hans96b, Ston90]
(large memory, complicated storage)
Parallel Processing
[Gupt89,Hell98]
AI
[Forg82,Mira87]
(smaller rule set)
The work proposed here is a combination of improvised version of some
of the modules mentioned above.
Contribution
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Data Structures
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If a large number of triggers are created, many of
them have almost the same format
Predicate Index Structure
Most important contribution
Concurrent processing
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Identified 4 levels of concurrency
Implemented token-level concurrency
Key Concepts – TiggerMan Architecture
Key Concepts – Trigger Structure
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Example: Stock ticker notification
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Create trigger T1 from stock
when stock.ticker = ‘GOOG’ and stock.value < 500
do notify_person(P1)
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Create trigger T2 from stock
when stock.ticker = ‘MSFT’ and stock.value < 30
do notify_person(P2)
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Create trigger T3 from stock
when stock.ticker = ‘ORCL’ and stock.value < 20
do notify_person(P3)
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Create trigger T4 from stock
when stock.ticker = ‘GOOG’
do notify_person(P4)
Key Concepts – Expression Signature
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Common structures in the condition of triggers
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T1: stock.ticker = ‘GOOG’ and stock.value < 500
T2: stock.ticker = ‘MSFT’ and stock.value < 30
T3: stock.ticker = ‘ORCL’ and stock.value < 20
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Expression Signature:
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E1: stock.ticker = const1 and stock.value < const2
 T4: stock.ticker = ‘GOOG’
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Expression Signature:
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E2: stock.ticker = const3
Key Concepts – A-Treat Network
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For each trigger condition
stock.ticker = const1 and stock.value < const2
Root
stock.ticker = const1
stock.value < const2
Node 1
Node 2
alpha-node
alpha-node
predicates
Key Concepts – Expression Signature
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Expression Signature Table
Ex.
ID
Data
Source
Signature
Constant
Description Table
Number of Constant
Constants Organization
E1
stock
…
const_e1
2
Main
Memory
E2
stock
…
const_e2
1
Main
memory
E1: stock.ticker = const1 and stock.value < const2
E2: stock.ticker = const3
Key Concepts – Constant Table
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Tables to include constants occurring in the
condition of triggers
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const_e1
Ex. ID
Trigger ID
Constant 1
Constant 2
Next Node
E1
T1
GOOG
500
Node 2
E1
T2
MSFT
30
Node 2
E1
T3
ORCL
20
Node 2
Rest
Const_e2
Ex. ID
Trigger ID
Constant 1
Next Node
E2
T4
GOOG
Null
T1: stock.ticker = ‘GOOG’ and stock.value < 500
T2: stock.ticker = ‘MSFT’ and stock.value < 30
T3: stock.ticker = ‘ORCL’ and stock.value < 20
Rest
T4: stock.ticker = ‘GOOG’
Key Concepts – Summary
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Expression Signature
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Common structure in a trigger
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A-treat network
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Network for trigger condition testing
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E1: stock.ticker = const1 and stock.value < const2
For a Trigger to fire, all conditions must be true
Constant Tables
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Constants for each Expressions Signature
Key Concepts - Predicate Index
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
Trigger ID Constant 1 Constant 2 Next
Node
T1
GOOG
500
Node 2
T2
MSFT
30
Node 2
T3
ORCL
20
Node 2
const_e1
Concurrency
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Concurrency
 Better scalability
 Even on single processor
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Identified elements that can be parallelized
 Token-level
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Condition-level
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Multiple rule actions fired at the same time
Data-level
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Multiple selection conditions tested concurrently
Rule-action-level
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Multiple tokens processed in parallel
Set of data values in the network processed in parallel
Implemented Token-level concurrency
Validation
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Important fact: If a large number of triggers
are created, many of them have almost the
same format
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Implemented as an Informix DataBlade
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No experimental comparisons
Assumptions
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If a large number of triggers are created,
many of them have almost the same format
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All distinct predicate structures fit into the
main memory
Rewrite today
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Validations
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Provide Experimental Comparisons
Test on real datasets
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Examples: Execution Trace
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Remove sections on TriggerMan Command
Language and Architecture
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Describe A-TREAT network
Summary
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If a large number of triggers are created,
many of them have almost the same format
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Number of distinct signatures is small enough
to fit into the main memory