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Theo Schlossnagle - @postwait Founder @Circonus

Monitoring the #DevOps Way (and the future of monitoring)

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DevOps

So many definitions of DevOps…

Applying software engineering practices to improve operations and infrastructure management.

Spreading operational acumen through the organization (specifically into product engineering)

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The days of “is it up” are long over.

Monitoring has changed Significant challenges in operability and accessibility

The days of “is it up” are long over.

Monitoring has changed Significant challenges in operability and accessibility

The days of “is it up” are long over.

Monitoring has changed Significant challenges in operability and accessibility

The days of “is it up” are long over.

Monitoring has changed Significant challenges in operability and accessibility

The days of “is it up” are long over.

Monitoring has changed Significant challenges in operability and accessibility

Managing risk and exploiting opportunity

Operability

Monitoring (data collection and analysis) is hard.

Now add “always available” and “always immediate.”

Many companies squander opportunity in engineering by building monitoring solutions instead of more direct market value.

ALWAYS ON AND ALWAYS WORKING… EVEN WHEN YOU’RE INFRASTRUCTURE ISN’T

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Embracing today’s engineering organizations

Accessibility

If it isn’t accessible via an API, it isn’t accessible.

Configuration, analysis, alerting, visualization… it all must be accessible or it simply won’t integrate.

This is how engineers work.

The dynamic nature of the the cloud forces your hand.

used with permission from Stacey Mason @stcymsn

Looking for information in the data

Millions of streamsIs simply too much to look at

We now must leveraging mathematics(e.g. statistics and machine learning) to surface interesting information

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Workloads and Latencies: histograms

Context is King

Solving this at scale

Once you solve the scale problem, what are the opportunities?

1000 measurements / second?How about 1 million? or 1 billion?

Imagine the insight.

Now imagine the data science problems.

Workloads and Latencies: histograms

Workloads and Latencies: modality

Workloads and Latencies: quantiles

The future is a math problem High velocity, low latency, with big data…

Highly challenging.