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The History of Metrics According to Me

Stephen Day
December 14, 2016

The History of Metrics According to Me

Metrics and monitoring are a time honored tradition for any engineering discipline. It is how we ensure the systems we use are working the way we expect. If this is a time honored tradition, why is it not a built into every piece of software we create, from the ground up? With software engineering, usually the trick to solving anything is to make it easier. By solving the hard parts of application metrics in Docker, we should make it more likely that metrics are a part of your services from the start.

Stephen Day

December 14, 2016

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  1. The History of Metrics According to Me Stephen Day Docker,

    Inc. Docker Meetup, SF December 2016 v1
  2. Models INFO[0012] response completed go.version=go1.6.3 http.request.host=localhost:5001 http.request.id=4db26096-a417-48e8-a759-ddd8ea66c3a0 http.request.method=GET http.request.remoteaddr= http.request.uri=/v2/

    http.request.useragent=curl/7.49.1 http.response.contenttype=application/json; charset=utf-8 http.response.duration=5.4388ms http.response.status=200 http.response.written=2 instance.id=7926875b-d3fb-41e4-bd75-2370a3e8a03c version=v2.5.1 Record-based
  3. Types of Metrics - Counters: cumulative number that only increases

    - Gauges: value that can go up or down - Summary/Histogram: Sample of observations https://prometheus.io/docs/concepts/metric_types/
  4. - For Contributors and Maintainers to better understand performance -

    For Users to better understand behavior of the daemon in their infrastructure - Implemented in 1.13 Docker Engine Metrics
  5. Application Metrics - Directly proxy your metrics out of the

    Docker daemon - Leverage built in integrated target discovery