Better Reliability through Observability (and Experimentation)

In this presentation, Julie Gunderson (Sr. Reliability Advocate at Gremlin), and I look at how to improve your service's reliability through experimentation.

This version of the talk was given at a KubeCon Europe (Valencia) in May 2022.

Transcript

1. Better Reliability through Observability (and Experimentation) Julie Gunderson, Gremlin & Kerim Satirli, HashiCorp
2. Who we are ▪ Sr. Reliability Advocate at ▪ devopsdays Boise organizer ▪ avid mushroom-hunter ▪ came to Spain by plane Julie Gunderson @julie_gund Gremlin
3. ▪ Sr. Developer Advocate at ▪ recovering conference organizer ▪ aerial photography aficionado ▪ came to Spain by plane Who we are Kerim Satirli @ksatirli
6. complex systems fail
7. OOPS WE DIDN’T TEST THAT GRAPHS AND TRACES AUTOMATED VISIBILITY SURVEILLANCE STATE 29 23 11 1 ? WHAT DOES "OBSERVABILITY" MEAN TO YOU? known-knowns known-unknowns uhm. ! marketing "
8. gather requirements build the thing release the thing experience incident $
9. experience incident restore service % detect incident
10. OOPS WE DIDN’T TEST THAT GRAPHS AND TRACES WHAT DOES "OBSERVABILITY" MEAN TO YOU? known-knowns known-unknowns INFORMATION YOU DIDN’T THINK YOU NEEDED BUT COULD ACTUALLY SOLVE YOUR PROBLEM unknown-unknowns 4 5
11. pillars of observability
12. If you can't log it, you can't investigate it.
13. If you can't trace it, you can't debug it.
14. If you can't measure it, you can't understand it.
15. If you can't measure it, you can't understand it. If you can’t interpret it, you can’t harden it.
16. trace, log, measure
17. restore service time to detect time to restore experience incident
18. experimentation
19. ▪ observe baseline metrics understand and document your system’s nominal state Science and Chaos
20. ▪ observe baseline metrics ▪ formulate hypothesis given a nominal state, does this function the way we expect it to? Science and Chaos
21. what parts of the system will be impacted by an experiment? ▪ observe baseline metrics ▪ formulate hypothesis ▪ understand blast radius Science and Chaos
22. when to stop experimenting and revert back to a nominal state? ▪ observe baseline metrics ▪ formulate hypothesis ▪ understand blast radius ▪ set abort conditions Science and Chaos
23. ▪ observe baseline metrics ▪ formulate hypothesis ▪ understand blast radius ▪ set abort conditions ▪ analyze the results what learnings can be derived from the experiment? Science and Chaos
24. ▪ observe baseline metrics ▪ formulate hypothesis ▪ understand blast radius ▪ set abort conditions ▪ analyze the results ▪ learn and improve share the results and derive actions from the data Science and Chaos
25. four golden signals
26. understand your recovery plan verify backups are known-good ⚠
27. how to simulate Latency the time it takes to service a request ▪ programmatically inject delays ▪ change DNS and network settings ▪ switch to different geo zones Signals and Simulations
28. Errors the rate of requests that fail to complete correctly how to simulate ▪ terminate services ▪ revoke access credentials ▪ change system clock and timezones Signals and Simulations
29. Traffic the demand that is placed on a system at any point how to simulate ▪ create traffic spikes with tooling ▪ change load-balancing to create hot spots ▪ re-deploy on over-subscribed compute Signals and Simulations
30. Saturation the measure of system utilization and constraints how to simulate ▪ alter scaling logic to delay triggering ▪ fill up empty disk space and memory ▪ run stress or consume.exe Signals and Simulations
31. Take-aways ▪ this was never about any one tool ▪ codify resources and processes ▪ method over madness ▪ culture breeds reliability (words matter)
32. Normal Accidents Living with High-Risk Technologies Charles Perrow 1984 Fatal Defect Chasing Killer Computer Bugs Ivars Peterson 1995 Accelerate Building and Scaling High-Perf Orgs Nicole Forsgren et al. 2018
33. Gracias!