行事後

導無責事後以學事且增系韌。

用

• 生產事故或服退後
• 近錯之後
• 查復現事
• 跨組傳學

入

• 必：事故詳（始終時、所影服、嚴）
• 必：事窗間之日誌、度、警之取
• 可：事中所用 runbook
• 可：通信日誌（Slack、PagerDuty）

行

一：集原始數據

聚事諸構件：

# 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

得： 日誌、度、警覆全事時。

敗： 數據不全→於報注缺。下次設更長留期。

二：建時線

按序重建：

## 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 |

得： 明、分分之序示何時何事。

敗： 時戳不合→確諸系用 NTP 且 UTC 日誌。

三：識貢因

用五何或魚骨析：

## 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

得： 多層因已識，避責。

敗： 析止於「工程師誤」→深挖。何使誤可發？

四：生動作項

建具可追之改：

## 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 |

得： 每項有主、期、明交付。

敗： 泛動作如「改測」不行。具體為宜。

五：寫與發報

用此模：

# 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.

得： 報於事後 48 時內發至組與干係人。

敗： 報遲逾 1 週→學失鮮。優先事後。

六：於 standup/retro 察動作

追動作進：

# 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

得： 動作追於項管具，每週察。

敗： 動作懈→事復。高優項派主管。

驗

• 時線全且序準
• 多貢因已識（非只一）
• 動作有主、期、優
• 報用無責語（無「X 致之」）
• 報於 48 時內發諸干係人
• 動作追於票系
• 4 週後察已排

忌

• 責文化：用「誰」語非「何/因」。焦系非人。
• 淺析：止於首因。必問「因」至少五次。
• 泛動作：「改監」非可為。「於某日加度 X 於板 Y」為宜。
• 無隨進：動作生而未察。設日曆提。
• 懼透明：藏事減學。廣分（守安界）。

參

• write-incident-runbook — 事中所引 runbook 之建
• configure-alerting-rules — 依事後發現改警