Embracing Failure and Learning from Our Mistakes with Effective Post Mortems
summary
Describes how Datadog runs blameless postmortems end-to-end: collect time-series metrics (they ingest ~1 trillion points/day), capture Slack control-call logs, build a timeline annotated with graphs, and run monthly company reviews. Recommends classifying signals as work/resource/event metrics, keeping fine-grained, tagged data long enough to investigate, and tracking action items in GitHub/Trello. Emphasizes quick notes after incidents, avoid blame, prioritize customer-impact fixes, and add tooling checks (e.g., command validation) that actually prevent repeat failures.
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chapters (10)
- 0:00Embracing Failure and Learning from Our Mistakes with Effective Post Mortems llan Rabinovitch, Datadog
- 2:58THE PROBLEMS WE WORK ON AT DATALOG ARE HARD AND OFTEN DON'T HAVE OBVIOUS. CLEAN- CUT SOLUTIONS. SO IT'S USEFUL TO CULTIVATE YOUR TROUBLESHOOTING SKILLS, NO MATTER WHAT ROLE YOU WORK IN.
- 17:00RESOURCE METRICS UTILIZATION SATURATION ERROR AVAILABILITY
- 18:454 QUALITIES OF GOOD METRICS Well-understood Granular Tagged by scope Long lived
- 24:41TECHNICAL ISSUES HAVE NON-TECHNICAL CAUSES HUMAN ELEMENT
- 28:08"ONE PICTURE IS WORTH TEN THOUSAND WORDS" CHINESE PROVERB
- 34:53SUMMARY: WHAT HAPPENED? Describe what happened here at a high-level- think of it as an abstract in a scientific paper. What was the impact on customers? What was the severity of the outage? What components were affected? What ultimately resolved the outage?
- 40:34WHY DID IT HAPPEN? Deep dive into the cause Examples from this incident
- 41:45HOW DO WE PREVENT IT IN THE FUTURE? Link to Github issues and Trello cards Now? Next? Later? Follow up notes
- 44:14RECAP: What happened summaryl? How did we detect it? How did we respond? Why did it happen deep dive/? Actionable next steps!
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