pjt222 Conduct Post-Mortem
Lead a blameless post-mortem to learn from incidents and improve system resilience.
When to Use
- After any production incident or service degradation
- Following a near-miss or close call
- When investigating recurring issues
- To share learnings across teams
Inputs
- Required: Incident details (start/end time, services affected, severity)
- Required: Access to logs, metrics, and alerts during the incident window
- Optional: Runbook used during incident response
- Optional: Communication logs (Slack, PagerDuty)
Procedure
Step 1: Collect Raw Data
Gather all artifacts from the incident:
# Export relevant logs (adjust timerange)
kubectl logs deployment/api-service \
--since-time="2025-02-09T10:00:00Z" \
--until-time="2025-02-09T11:30:00Z" > incident-logs.txt
# Export Prometheus metrics snapshot
curl -G 'http://prometheus:9090/api/v1/query_range' \
--data-urlencode 'query=rate(http_requests_total{job="api"}[5m])' \
--data-urlencode 'start=2025-02-09T10:00:00Z' \
--data-urlencode 'end=2025-02-09T11:30:00Z' \
--data-urlencode 'step=15s' > metrics.json
# Export alert history
amtool alert query --within=2h alertname="HighErrorRate" --output json > alerts.json
Expected: Logs, metrics, and alerts covering the full incident timeline.
On failure: If data is incomplete, note gaps in the report. Set up longer retention for next time.
Step 2: Build the Timeline
Create a chronological reconstruction:
## Timeline (all times UTC)
| Time | Event | Source | Actor |
|----------|-------|--------|-------|
| 10:05:23 | First 5xx errors appear | nginx access logs | - |
| 10:06:45 | High error rate alert fires | Prometheus | - |
| 10:08:12 | On-call engineer paged | PagerDuty | System |
| 10:12:00 | Engineer acknowledges alert | PagerDuty | @alice |
| 10:15:30 | Database connection pool exhausted | app logs | - |
| 10:18:45 | Database queries identified as slow | pganalyze | @alice |
| 10:22:10 | Cache layer deployed as mitigation | kubectl | @alice |
| 10:35:00 | Error rate returns to normal | Prometheus | - |
| 10:40:00 | Incident marked resolved | PagerDuty | @alice |
Expected: A clear, minute-by-minute sequence showing what happened and when.
On failure: Timestamp mismatches. Ensure all systems use NTP and log in UTC.
Step 3: Identify Contributing Factors
Use the Five Whys or fishbone analysis:
## Contributing Factors
### Immediate Cause
- Database connection pool exhausted (max 20 connections)
- Query introduced in v2.3.0 deployment lacked index
### Contributing Factors
1. **Monitoring Gap**: Connection pool utilization not monitored
2. **Testing Gap**: Load testing didn't include new query pattern
3. **Runbook Gap**: No documented procedure for DB connection issues
4. **Capacity Planning**: Pool size unchanged despite 3x traffic growth
### Systemic Issues
- No pre-deployment query plan review
- Database alerts only fire on total failure, not degradation
Expected: Multiple layers of causation identified, avoiding blame.
On failure: If analysis stops at "engineer made a mistake", dig deeper. What allowed that mistake?
Step 4: Generate Action Items
Create concrete, trackable improvements:
## Action Items
| ID | Action | Owner | Deadline | Priority |
|----|--------|-------|----------|----------|
| AI-001 | Add connection pool metrics to Grafana | @bob | 2025-02-16 | High |
| AI-002 | Create runbook: DB connection saturation | @alice | 2025-02-20 | High |
| AI-003 | Add DB query plan check to CI/CD | @charlie | 2025-03-01 | Medium |
| AI-004 | Review and adjust connection pool size | @dan | 2025-02-14 | High |
| AI-005 | Implement DB slow query alerts (<100ms) | @bob | 2025-02-23 | Medium |
| AI-006 | Add load testing for new query patterns | @charlie | 2025-03-15 | Low |
Expected: Each action has an owner, deadline, and clear deliverable.
On failure: Vague actions like "improve testing" won't get done. Make specific.
Step 5: Write and Distribute Report
Use this template structure:
# Post-Mortem: API Service Degradation (2025-02-09)
**Date**: 2025-02-09
**Duration**: 1h 35min (10:05 - 11:40 UTC)
**Severity**: P1 (Critical service degraded)
**Authors**: @alice, @bob
**Reviewed**: 2025-02-10
## Summary
The API service experienced elevated error rates (40% of requests) due to
database connection pool exhaustion. Service was restored by deploying a
cache layer. No data loss occurred.
## Impact
- 40,000 failed requests over 1.5 hours
- 2,000 customers affected
- Revenue impact: ~$5,000 (estimated)
## Root Cause
Query introduced in v2.3.0 deployment performed a full table scan due to
missing index. Under increased load, this saturated the connection pool.
[... timeline, contributing factors, action items as above ...]
## What Went Well
- Alert fired within 90 seconds of first errors
- Mitigation deployed quickly (10 minutes from page to fix)
- Communication to customers was clear and timely
## Lessons Learned
- Database monitoring is insufficient; need connection-level metrics
- Load testing must cover new query patterns, not just volume
- Connection pool sizing hasn't kept pace with traffic growth
## Prevention
See Action Items above.
Expected: Report shared with team and stakeholders within 48 hours of incident.
On failure: If report delays exceed 1 week, insights grow stale. Prioritize post-mortems.
Step 6: Review Action Items in Standup/Retros
Track action item progress:
# Create GitHub issues from action items
gh issue create --title "AI-001: Add connection pool metrics" \
--body "From post-mortem PM-2025-02-09. Owner: @bob. Deadline: 2025-02-16" \
--label "post-mortem,observability" \
--assignee bob
# Set up recurring reminder
# Add to team calendar: Weekly review of open post-mortem items
Expected: Action items tracked in project management tool, reviewed weekly.
On failure: If action items languish, incidents will recur. Assign executive sponsor for high-priority items.
Validation
- Timeline is complete and chronologically accurate
- Multiple contributing factors identified (not just one)
- Action items have owners, deadlines, and priorities
- Report uses blameless language (no "X caused the issue")
- Report distributed to all stakeholders within 48 hours
- Action items tracked in ticketing system
- Follow-up review scheduled for 4 weeks out
Common Pitfalls
- Blame culture: Using "who" language instead of "what/why". Focus on systems, not people.
- Shallow analysis: Stopping at the first cause. Always ask "why" at least 5 times.
- Vague action items: "Improve monitoring" is not actionable. "Add metric X to dashboard Y by date Z" is.
- No follow-through: Action items created but never reviewed. Set calendar reminders.
- Fear of transparency: Hiding incidents reduces learning. Share widely (within appropriate security boundaries).
Related Skills
write-incident-runbook- create runbooks referenced during incidentsconfigure-alerting-rules- improve alerts based on post-mortem findings