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Lessons from Automatic Incident Resolution for a Million Databases

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Greg Burek
Greg Burek

SRECon EU 2016 Session

Engineering
1 of 30
SRECon EU, July 2016 Greg Burek Lessons from Automatic Incident Resolution for a Million Databases
The Twelve-Factor App
Department of Data
Postgresql Redis Kafka
~ Million Databases Tens of thousands of AWS Instances
Some Databases Hundreds of AWS Instances
“The goal is to build systems that can scale linearly with machines & sub-linearly with people” - Caitie McCaffrey Tackling Alert Fatigue
Monitor and alert on your business
Monitor and alert on your business Usually, don’t alert on machine specific metrics
Write runbooks and playbooks
Turn playbooks into code
“The goal is not to never get paged, the goal is to never get paged for the same thing twice” - Astrid Atkinson Engineering for the long game
Verify monitoring before restarting the world
Circuit breakers
Automation can’t handle the unknown Wake someone up on exceptions and timeouts
Have a REPL/console
Aggregate and review trends
Humans can break Automation can be simplistic Humans + Automation for a resilient and operable system
1. Monitor and alert on your business 2. Write playbooks 3. Make playbooks into automation 4. Checks and balances of automation 5. Circuit breakers 6. Alert on exceptions and timeouts 7. Admin console 8. Aggregate and review trends
gregburek@heroku.com @gregburek
State Machines

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Lessons from Automatic Incident Resolution for a Million Databases

Editor's Notes

  1. Hello, My name is Greg Burek and today I am going to be talking about Lessons from automatic incident resolution for a million databases. Let's get right into it
  2. Heroku
  3. Heroku
  4. Soup to nuts small team that handles all data services, from orchestration, AWS account
  5. Heroku
  6. State machines