A Performance study on Operator-based stream processing systems
Miyuru Dayarathna, Souhei Takeno, Toyotaro Suzumura Department of Computer Science Tokyo Institute of Technology Japan
Stream Computing Systems
Insights from data in motion ◦ It is impossible to store data on disk ◦ The volume of the data is very large
Process data on-the-fly in-memory Route keyless input events
OP 1
Join the serve and click events
OP 2
BotFilter
OP 3
Compute the correct click throughput rate
OP 4
Streaming Click-Through Rate Computation
2
Essence of our Performance Study System S (IBM) and S4 (Yahoo) Four benchmarks (60 application Scenarios) Five metrics
3
12
140
Thousands
Thousands
Results - Throughput Throughput observed for four applications on S4
Throughput observed for five applications on System S CDR
120
10
VWAP
6 CDR Optimized
4
VWAP Twitter
2
Micro-benchmark
Throughput (Tuples\s)
Throughput (Events\s)
100 8
Micro-benchmark CDR Optimized
80 Twitter
60 40 20
CDR
0
0 0
2
4
6
8
Number of Nodes
(c)
10
12
14
0
2
4
6
8
10
12
14
Number of Nodes
(d)
4
Essence of our Performance Study System S (IBM) and S4 (Yahoo) Four benchmarks (60 application Scenarios) Five metrics Conclusions on Stream Processing system architectures
A Performance study on Operator-based stream processing systems
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ios) on System S and S4 gave us sufficient insight to what kind of processing happen in the both the systems. It became clear from the throughput comparisons ...
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computer-vision techniques and large-scale-data-stream processing algorithms to .... sub-fingerprint with the maximum score is the best match on that spectral image. .... Finding interesting associations without support pruning. Knowledge and ...
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