Parallel Event Processing for Content-Based
Publish/Subscribe Systems
Amer Farroukh
Department of Electrical and Computer Engineering
University of Toronto
Joint work with Elias Ferzli, Naweed Tajuddin, and Hans-Arno Jacobsen
DEBS 2009
Motivation
• Event processing is ubiquitous in enterprise-scale
applications (Fraud detection, Data analysis)
• Network security monitoring and analysis tools require
Gigabit per second speed (Application-layer firewalls)
• Selective dissemination of information for Internetscale applications (RSS, XML, Xpath)
• These systems need to support thousands of users and
process millions of events
• Achieving Scalability and high performance under
excessive load is a challenging problem
• Matching engine is the most computation intensive
function in event processing
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DEBS 2009
How to support high data-processing
rates?
• Choose an existing, powerful matching
algorithm
• Leverage chip multi-processors
• Increase throughput or reduce matching time
• Evaluate multi-threading vs. software
transactional memory
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DEBS 2009
Outline
•
•
•
•
Related work
Matching algorithm
Parallelization techniques
Implementation and results
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Sequential Matching Algorithms
• Single phase: A_TREAT [E.H., 1992]
– Predicates are complied into a test network
– Subscriptions may appear in one or several leaves
– Poor locality, space consuming, hard to maintain
• Two phase: SIFT [T.Y., 2000]
– Predicates are evaluated in the first phase
– Subscriptions are matched in the second phase
– Predicates and subscription are indexed
• Algorithm used: Filtering Algorithms [F.F., 2001]
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DEBS 2009
Matching Algorithm
E
P1
Price
P2
Color
Quantity
Phase 1
0 1
0 0 0
1 0 10 0 1
0
Phase 2
Ap1
C1
C2
Ap2
C1
C2
C1
C2
Ap3
Ap4
Ap5
.
.
.
DEBS 2009
C3
S1
S5
C3
S9
6
Multiple Events Independent Processing
Thread 1
E1
P1
E2
P2
P1
Price
Color
0 1
0 0 0
1 0 0 0 1
0
0 1
0 0 0
1 0
1 0 0 1
0
Ap1
C1
C2
S7
Ap2
C1
C2
C1
C2
Ap4
S8
Ap5
.
.
.
P2
Quantity
S1
Ap3
Thread 2
DEBS 2009
C3
S3
S2
C3
S9
7
Single Event Collaborative Processing
Thread 1
E
P1
Price
Thread 2
P2
Color
0 0 0 0
1 0 0 0 0
Quantity
0 1
0 0 0 0 0 0 0
1
0 1
0 0 0
1 0 0 0 1
0
S1
Ap1
C1
C2
Ap2
C1
C2
C1
C2
Ap3
Ap4
S8
Ap5
.
.
.
DEBS 2009
C3
S2
C3
8
Multiple Events Collaborative Processing
Group 1
T1
T2
E1
P1
P2
P1
Price
0 0
1 0
Group 2
T3
T4
E2
Color
P2
Quantity
0 0 0
1
1
0 0 0
0
1 0 1
0
0
1 1
0 0
S1
S3
Ap1
C1
C2
Ap2
C1
C2
C1
C2
Ap3
S7
0 0 0
1
Ap4
Ap5
.
.
.
DEBS 2009
C3
S2
S4
C3
S9
9
Implementation Setup
• Synchronization
– Static
– Locks
– Software transactional memory (STM)
• Machine
– 2.33GHz quad-core Xeon processors
– 32KB L1 cache and 4MB L2 cache
• Workload
Number of Subscriptions
1M – 6M
Average Predicates per Subscription
10
Predicate Range
1 - 15
Number of Events
5000
Average Attributes per Event
50
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DEBS 2009
Multiple Events Independent Processing
Analysis
Linear Throughput and Constant
Average Matching Time
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DEBS 2009
Single Event Collaborative Processing
Analysis
Lock Implementation is best
Bit vector size limits scalability
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DEBS 2009
Multiple Events Collaborative Processing
Analysis
Threads can be allocated based on
system requirements and load
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DEBS 2009
Conclusions
•
•
•
•
•
Parallel matching engine is a promising solution
Over 1600 events/s with 6M subs
Matching time vs. throughput
Lock-based implementation is more efficient
HTM is a potential candidate for enhancing
speed and potential ease of implementation
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DEBS 2009
DEBS 2009
Predicate Tables (Phase 1)
S1: quantity = 2 , price < 30
QUANTITY
1
S2: quantity > 4 , price = 20
2
EQUAL
3
4
5
1
LESS
GREATER
3
NOT EQUAL
PRICE
EQUAL
10
20
30
40
50
4
2
LESS
GREATER
NOT EQUAL
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DEBS 2009
Subscription Clusters (Phase 2)
Ap1
S1
S2
S3
S4
P1
P2
P3
Ap2
S5
P4
.
.
.
ApN
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Time Profiling
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Block Size
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DEBS 2009
Subscriptions Effect
SE-CP
ME-IP
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DEBS 2009