Introduction of Handshake joins in the paper "How Soccer Players Would do Stream Joins".

1. How Soccer Players
Would do Stream Joins
3/4/2015 @nobu_k
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2. Who?
久保田展行 (@nobu_k)
CTO@Preferred Networks America, Inc.
Speciality
DBMS, Search engine
Distributed Systems (consensus)
beatmania IIDX SP/DP皆伝 (DPメイン)
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3. How Soccer Players
Would do Stream Joins
Jens Teubner, Rene Mueller, SIGMOD 2011
Handshake Join
Window-based stream joins supporting any join predicate
Very high degrees of parallelism
multi-core CPUs
FPGA
Massively Parallel Processor Arrays (MPPAs)
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4. Joins
Joins(⋈) combine two or more relations(tables) in RDBMS
A join is a cross product of relations followed by a selection(σ)
Many methods
Nested-loops joins
Sort-merge joins
(Recursive, hybrid) hash joins
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5. Stream Joins
Problems
Unbounded "infinite" input data
Solution: (sliding) window-based joins
tuple-based/time-based
Latency of the output
Solution: online, symmetric evaluation
How can it be scalable?
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6. Handshake Joins
Streams flow by each other in opposite directions.
Each core locally evaluates tuples.
Core 1 Core 2 Core 3
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7. A newly arrived tuple( ) will be compared to all
tuples( ) in the other stream in the same core.
Any comparison algorithm(predicate) can be used.
Evaluation Strategy
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8. Strategies
Lock Step Forwarding
Two-Phase Forwarding using Async-MQ
Asymmetric protocol
Synchronization
b
a
a and b miss each other.
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9. Two-Phase Forwarding
Using Asynchronous Message Queue
b
a
b
a
b
Leaving the tuple with a special mark.
FIFO queue
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1.
2.

10. Two-phase forwarding: ACK
b
a
b
b
a
b
b
When the left core receives tuple b,
it sends an ack to the right core
before sending any other tuples.
The right core deletes b
when it receives the ack.
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2.
3.

11. Two-Phase Forwarding:
when a and b miss each other
bb
a
bb
a
a will be compared to tuples
in the right core including b.
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4.
5.

12. Load Balancing
Automatic load balancing without centralized control.
Each core can handle an arbitrary number of tuples.
Core 1 Core 2 Core 3
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13. Software Implementation
AMD Opteron 6174 2.2GHz
libnuma
quoted from page 8
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14. Scalability
page 8 page 9
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15. FPGA Implementation
Assume the system has to provide a throughput of 500ktuples/sec with a window size of 100 tuples.
Config- urations with 1, 2, 5, and 10 join cores can guarantee this throughput if operated at clock
frequencies of 50, 25, 10, or 5 MHz, respectively.
from page 10
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16. Summary
Handshake join
Window-based stream join
Flexible and scalable
Working well with FPGA
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