行事後之審

引無責事後之審以學於事且增系之韌。

用時

• 產境有事或服降之後
• 近失或險過之後
• 察重現之問時
• 跨團共所學

入

• 必：事詳（起止時、受影之服、重）
• 必：事窗中誌、量、警之訪
• 可選：事中所用之行冊
• 可選：通誌（Slack、PagerDuty）

法

第一步：收原資

聚事之諸品：

# 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： 誌、量、警涵全事線。

敗則： 資不全則注缺於報。下次設更長之留。

第二步：建時線

生時序之復：

## 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： 明分分之序示何事於何時。

敗則： 時戳不合。確諸系用 NTP 而誌 UTC。

第三步：識助因

用五何或魚骨之析：

## 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： 多層因已識而無責。

敗則： 若析止於「工師誤」，深掘。何令此誤發？

第四步：生行項

立具體可追之改：

## 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： 每行有主、期、明產。

敗則： 含糊如「改測」不行。宜具體。

第五步：書而分報

用此範構：

# 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： 報於事後四十八時內分於團與相關。

敗則： 若報遲逾一週，見解將陳。先於事後之審。

第六步：於日會/回顧察行項

追行項之進：

# 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： 行項追於項目管具，週察之。

敗則： 若行項荒，事必重。為高危項命執助。

驗

• 時線全且時序準
• 多助因已識（非一）
• 行項有主、期、先
• 報用無責之言（無「X 致此問」）
• 報於四十八時內分於諸相關
• 行項追於票系
• 四週後之察已排

陷

• 責之文：用「誰」而非「何/因」之言。專於系，非人。
• 淺析：止於初因。必問「因」至少五次。
• 含糊行項：「改察」非可行。「於日 Z 加量 X 於板 Y」乃可行。
• 不隨：行項建而不察。設日曆之提。
• 畏明：匿事減學。廣分（於合安界內）。

參

• write-incident-runbook - 建事中所引之行冊
• configure-alerting-rules - 依事後之得改警