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Its-Not-in-Production-Unless-its-Monitored.md

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Presenter: Joseph Ruscio

Bio

Joseph Ruscio is a Co-Founder and CTO at Librato, Inc. He’s responsible for the company’s technical strategy, architecture, and generally hacks on all levels of the product. He’s a Ruby enthusiast and the author of the aggregate and rack-test-rest gems. @josephruscio on Twitter and Github.

Abstract

In the 21st century successful teams are data-driven. We’ll present a complete introduction to everything you need to start monitoring your service at every level from business drivers to per-request metrics in Rails/Rack, down to server memory/cpu. Provides a high-level overview of the fundamental components that comprise a holistic monitoring system and then drills into real-world examples with tools like ActiveSupport::Notifications, statsd/rack-statsd, and CollectD. Also covers best practices for active alerting on custom monitoring data.

Summary

TODO

Quotes

  • "Monitoring is to operations as unit tests are to development." - @josephruscio
  • "New code gets monitoring the same way new code gets tests." - @josephruscio
  • "monitoring == tests" - @josephruscio

Notes

From @benjaminoakes

  • He works for Librato Metrics
  • Evite
    • Started in 1998
    • Grig Gheorghi said (paraphrased) It's not in production until it's monitored (TODO twitter link"
  • SaaS 2002: Everything on your own hardware
  • SaaS 2032: outsourced to the cloud
    • less than (or equal to) 1 ops person
    • OSS
    • Server instances come and go; "ephemeral infrastructure"
    • More change, worse tools
  • Continous Deployment
    • About half in this room do it
    • Weekly release: everyone's scrambling to get something out
    • Continuous Integration (CI); run test all the time
    • "One-click" deploy
    • Feature-flagging: bleed users onto the new functionality
    • But bugs still make it through, so... we need monitoring
    • "monitoring is to ops as is unit tests is to development"
    • Find "chunky bacon" in the wild :)
  • Monitoring servies
    • A lot
    • Some good; some bad
    • Anti-pattern: use a bunch of off-the-shelf tools. Hard to correlate. Leads to "#monitoringsucks" (a movement). TODO find irc, github repo, hashtag on Twitter
  • Metrics: business, performance, resources, network
    • If you were to use a monolithic tool, you collect and then get something for analysis... but crosscutting is hard.
    • Cost of collection has to be near-zero (like for unit tests)
    • "New code gets monitoring the same way new code gets tests"
    • Logging
      • etsy/logster -- has a log counter/statistical analysis
      • logstash/logstash - mimics syslogd
      • Papertrail
    • ... notifications (FIXME missed this)
    • eric/metriks
  • Aggregation
    • Cheap collection... now what?
    • etsy/statsd - ~300 SLOC node.js. Anything instrumenting gets tracked over UDP packets.
    • Defined a (now standard) protcol
    • StatsD clients
      • zebrafishlabs/??? TODO
      • github/rack-statsd
      • shopify/??? TODO
    • Lots of server implementations too if you don't want to use node.js
  • Storage (persistance)
    • RRDTool
      • Default choice
      • "Round-Robin Database Tool"
      • Constant size buffer
    • Other, more interesting solutions
      • Graphite
        • Key/value pairs. Hierarchy availablbe in DNS style names "foo.bar.baz"
        • Can be a lot of data
      • Another solution for a lot of data: OpenTSDB
        • Based on HBase (Hadoop)
    • SaaS: Librato Metrics and others
  • Correlation
    • Now collection and storage is cheap... how do you use it?
    • Metrics -> annotations
    • Allow for arbitrary combinations of data
  • Dashboards
    • Like a wiki for shared understanding
    • Everybody on same page
    • Good if on a wall people see
    • Shameless plug: I wrote a Chrome Extension called TabCarousel for something like this
  • Alerts
    • Tuning alerts
    • Threshold is imporant: trigger, cancel, rearm
    • Aberrant Behavior: "???-Withers algorithm" TODO not sure of name
      • "Triple-exponential smoothing"
  • Takeaways
    • monitoring == tests
    • Arbitrary correlations
    • Dashboards
    • Alerts

External Links