Bottleneck analysis - Devopsdays Silicon Valley 2013

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Adrian Cockcroft

Lightning talk about the problems of efficiently feeding drinks to conference attendees

Technology Business

Bottle delivery data
Interval Response Time Throughput
10 3.1 22
20 1.2 41
30 7.9 32
… … …

Grab some data (using R)
beer <-
read.csv(url("http://staash.com/beer_operation
s.csv"))
response <- beer[,2]
plot(response, type="S",ylab=”response”)

Analysis
> summary(response)
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.909 2.550 2.820 3.086 3.214 67.680
> quantile(response,c(0.95,0.99))
95% 99%
4.149556 6.922115
> sd(response)
1.941328
> mean(response) + 2 * sd(response)
6.968416

chp(throughput,response,q=1.0)
(See http://perfcap.blogspot.com/search?q=chp)

Scalability plots generated using appdynamics.com

Well behaved Lock Contention
Oscillating, thread shortage
Looping autoscaled
Bottlenecks

http://perfcap.blogspot.com/search?q=chp
@adrianco

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