Appendix D - Example Postmortem

  1. Table of Contents
  2. Foreword
  3. Preface
  4. Part I - Introduction
  5. 1. Introduction
  6. 2. The Production Environment at Google, from the Viewpoint of an SRE
  7. Part II - Principles
  8. 3. Embracing Risk
  9. 4. Service Level Objectives
  10. 5. Eliminating Toil
  11. 6. Monitoring Distributed Systems
  12. 7. The Evolution of Automation at Google
  13. 8. Release Engineering
  14. 9. Simplicity
  15. Part III - Practices
  16. 10. Practical Alerting
  17. 11. Being On-Call
  18. 12. Effective Troubleshooting
  19. 13. Emergency Response
  20. 14. Managing Incidents
  21. 15. Postmortem Culture: Learning from Failure
  22. 16. Tracking Outages
  23. 17. Testing for Reliability
  24. 18. Software Engineering in SRE
  25. 19. Load Balancing at the Frontend
  26. 20. Load Balancing in the Datacenter
  27. 21. Handling Overload
  28. 22. Addressing Cascading Failures
  29. 23. Managing Critical State: Distributed Consensus for Reliability
  30. 24. Distributed Periodic Scheduling with Cron
  31. 25. Data Processing Pipelines
  32. 26. Data Integrity: What You Read Is What You Wrote
  33. 27. Reliable Product Launches at Scale
  34. Part IV - Management
  35. 28. Accelerating SREs to On-Call and Beyond
  36. 29. Dealing with Interrupts
  37. 30. Embedding an SRE to Recover from Operational Overload
  38. 31. Communication and Collaboration in SRE
  39. 32. The Evolving SRE Engagement Model
  40. Part V - Conclusions
  41. 33. Lessons Learned from Other Industries
  42. 34. Conclusion
  43. Appendix A. Availability Table
  44. Appendix B. A Collection of Best Practices for Production Services
  45. Appendix C. Example Incident State Document
  46. Appendix D. Example Postmortem
  47. Appendix E. Launch Coordination Checklist
  48. Appendix F. Example Production Meeting Minutes
  49. Bibliography

Example Postmortem

Shakespeare Sonnet++ Postmortem (incident #465)

Date: 2015-10-21

Authors: jennifer, martym, agoogler

Status: Complete, action items in progress

Summary: Shakespeare Search down for 66 minutes during period of very high interest in Shakespeare due to discovery of a new sonnet.

Impact:163 Estimated 1.21B queries lost, no revenue impact.

Root Causes:164 Cascading failure due to combination of exceptionally high load and a resource leak when searches failed due to terms not being in the Shakespeare corpus. The newly discovered sonnet used a word that had never before appeared in one of Shakespeare’s works, which happened to be the term users searched for. Under normal circumstances, the rate of task failures due to resource leaks is low enough to be unnoticed.

Trigger: Latent bug triggered by sudden increase in traffic.

Resolution: Directed traffic to sacrificial cluster and added 10x capacity to mitigate cascading failure. Updated index deployed, resolving interaction with latent bug. Maintaining extra capacity until surge in public interest in new sonnet passes. Resource leak identified and fix deployed.

Detection: Borgmon detected high level of HTTP 500s and paged on-call.

Action Items:165

Action Item Type Owner Bug

Update playbook with instructions for responding to cascading failure

mitigate

jennifer

n/a DONE

Use flux capacitor to balance load between clusters

prevent

martym

Bug 5554823 TODO

Schedule cascading failure test during next DiRT

process

docbrown

n/a TODO

Investigate running index MR/fusion continuously

prevent

jennifer

Bug 5554824 TODO

Plug file descriptor leak in search ranking subsystem

prevent

agoogler

Bug 5554825 DONE

Add load shedding capabilities to Shakespeare search

prevent

agoogler

Bug 5554826 TODO

Build regression tests to ensure servers respond sanely to queries of death

prevent

clarac

Bug 5554827 TODO

Deploy updated search ranking subsystem to prod

prevent

jennifer

n/a DONE

Freeze production until 2015-11-20 due to error budget exhaustion, or seek exception due to grotesque, unbelievable, bizarre, and unprecedented circumstances

other

docbrown

n/a TODO

Lessons Learned

What went well

  • Monitoring quickly alerted us to high rate (reaching ~100%) of HTTP 500s
  • Rapidly distributed updated Shakespeare corpus to all clusters

What went wrong

  • We’re out of practice in responding to cascading failure
  • We exceeded our availability error budget (by several orders of magnitude) due to the exceptional surge of traffic that essentially all resulted in failures

Where we got lucky166

  • Mailing list of Shakespeare aficionados had a copy of new sonnet available
  • Server logs had stack traces pointing to file descriptor exhaustion as cause for crash
  • Query-of-death was resolved by pushing new index containing popular search term

Timeline167

2015-10-21 (all times UTC)

  • 14:51 News reports that a new Shakespearean sonnet has been discovered in a Delorean’s glove compartment
  • 14:53 Traffic to Shakespeare search increases by 88x after post to /r/shakespeare points to Shakespeare search engine as place to find new sonnet (except we don’t have the sonnet yet)
  • 14:54 OUTAGE BEGINS — Search backends start melting down under load
  • 14:55 docbrown receives pager storm, ManyHttp500s from all clusters
  • 14:57 All traffic to Shakespeare search is failing: see https://monitor
  • 14:58 docbrown starts investigating, finds backend crash rate very high
  • 15:01 INCIDENT BEGINS docbrown declares incident #465 due to cascading failure, coordination on #shakespeare, names jennifer incident commander
  • 15:02 someone coincidentally sends email to shakespeare-discuss@ re sonnet discovery, which happens to be at top of martym’s inbox
  • 15:03 jennifer notifies shakespeare-incidents@ list of the incident
  • 15:04 martym tracks down text of new sonnet and looks for documentation on corpus update
  • 15:06 docbrown finds that crash symptoms identical across all tasks in all clusters, investigating cause based on application logs
  • 15:07 martym finds documentation, starts prep work for corpus update
  • 15:10 martym adds sonnet to Shakespeare’s known works, starts indexing job
  • 15:12 docbrown contacts clarac & agoogler (from Shakespeare dev team) to help with examining codebase for possible causes
  • 15:18 clarac finds smoking gun in logs pointing to file descriptor exhaustion, confirms against code that leak exists if term not in corpus is searched for
  • 15:20 martym’s index MapReduce job completes
  • 15:21 jennifer and docbrown decide to increase instance count enough to drop load on instances that they’re able to do appreciable work before dying and being restarted
  • 15:23 docbrown load balances all traffic to USA-2 cluster, permitting instance count increase in other clusters without servers failing immediately
  • 15:25 martym starts replicating new index to all clusters
  • 15:28 docbrown starts 2x instance count increase
  • 15:32 jennifer changes load balancing to increase traffic to nonsacrificial clusters
  • 15:33 tasks in nonsacrificial clusters start failing, same symptoms as before
  • 15:34 found order-of-magnitude error in whiteboard calculations for instance count increase
  • 15:36 jennifer reverts load balancing to resacrifice USA-2 cluster in preparation for additional global 5x instance count increase (to a total of 10x initial capacity)
  • 15:36 OUTAGE MITIGATED, updated index replicated to all clusters
  • 15:39 docbrown starts second wave of instance count increase to 10x initial capacity
  • 15:41 jennifer reinstates load balancing across all clusters for 1% of traffic
  • 15:43 nonsacrificial clusters’ HTTP 500 rates at nominal rates, task failures intermittent at low levels
  • 15:45 jennifer balances 10% of traffic across nonsacrificial clusters
  • 15:47 nonsacrificial clusters’ HTTP 500 rates remain within SLO, no task failures observed
  • 15:50 30% of traffic balanced across nonsacrificial clusters
  • 15:55 50% of traffic balanced across nonsacrificial clusters
  • 16:00 OUTAGE ENDS, all traffic balanced across all clusters
  • 16:30 INCIDENT ENDS, reached exit criterion of 30 minutes’ nominal performance

163Impact is the effect on users, revenue, etc.

164An explanation of the circumstances in which this incident happened. It’s often helpful to use a technique such as the 5 Whys [Ohn88] to understand the contributing factors.

165"Knee-jerk" AIs often turn out to be too extreme or costly to implement, and judgment may be needed to re-scope them in a larger context. There’s a risk of over-optimizing for a particular issue, such as adding specific monitoring/alerting when reliable mechanisms like unit tests can catch problems much earlier in the development process.

166This section is really for near misses, e.g., “The goat teleporter was available for emergency use with other animals despite lack of certification.”

167A “screenplay” of the incident; use the incident timeline from the Incident Management document to start filling in the postmortem’s timeline, then supplement with other relevant entries.

168Useful information, links, logs, screenshots, graphs, IRC logs, IM logs, etc.

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