postmortem-writing

事後分析の作成

組織的な学習を促し、インシデントの再発を防ぐ、効果的で責任追及を伴わない事後分析を作成するための包括的なガイドです。

次の場合はこのスキルを使用しないでください

• タスクが事後分析の作成に関係しない場合
• この範囲外の別のドメインやツールが必要な場合

手順

• 目標、制約、必要な入力を明確にします。
• 関連するベストプラクティスを適用し、結果を検証します。
• 実行可能な手順と検証方法を提示します。
• 詳細な例が必要な場合は、resources/implementation-playbook.mdを開きます。

次の場合にこのスキルを使用してください

• インシデント発生後のレビューを実施する場合
• 事後分析の文書を作成する場合
• 責任追及を伴わない事後分析ミーティングを進行する場合
• 根本原因と寄与要因を特定する場合
• 実行可能なフォローアップ項目を作成する場合
• 組織的な学習文化を築く場合

主な概念

1. 責任追及を伴わない文化

責任追及型	責任追及を伴わない
「誰がこれを引き起こしたのか？」	「どのような条件がこれを可能にしたのか？」
「誰かがミスをした」	「システムがこのミスを可能にした」
個人を罰する	システムを改善する
情報を隠す	学びを共有する
発言することへの恐れ	心理的安全性

2. 事後分析を実施する契機

• SEV1またはSEV2のインシデント
• 15分を超える顧客向けサービスの停止
• データ損失またはセキュリティインシデント
• 深刻な事態になり得たニアミス
• 前例のない障害モード
• 通常とは異なる対応が必要だったインシデント

クイックスタート

事後分析のタイムライン

Day 0: Incident occurs
Day 1-2: Draft postmortem document
Day 3-5: Postmortem meeting
Day 5-7: Finalize document, create tickets
Week 2+: Action item completion
Quarterly: Review patterns across incidents

以下のインシデント数値は架空の例です。出典のある観測値に置き換え、推定値や不明な点にはその旨を明記し、デプロイ時刻、影響開始時刻、検知時刻、復旧時刻を区別してください。復旧したという事実だけから、データ損失がなかったと推測しないでください。

テンプレート

テンプレート1：標準的な事後分析

# Postmortem: [Incident Title]

**Date**: 2024-01-15
**Authors**: @alice, @bob
**Status**: Draft | In Review | Final
**Incident Severity**: SEV2
**Incident Duration**: 47 minutes

## Executive Summary

On January 15, 2024, the payment processing service experienced a 47-minute outage affecting approximately 12,000 customers. The root cause was a database connection pool exhaustion triggered by a configuration change in deployment v2.3.4. The incident was resolved by rolling back to v2.3.3 and increasing connection pool limits.

**Impact**:
- 12,000 customers unable to complete purchases
- Estimated revenue loss: $45,000
- 847 support tickets created
- No data loss or security implications

## Timeline (All times UTC)

| Time | Event |
|------|-------|
| 14:23 | Deployment v2.3.4 completed to production |
| 14:31 | First alert: `payment_error_rate > 5%` |
| 14:33 | On-call engineer @alice acknowledges alert |
| 14:35 | Initial investigation begins, error rate at 23% |
| 14:41 | Incident declared SEV2, @bob joins |
| 14:45 | Database connection exhaustion identified |
| 14:52 | Decision to rollback deployment |
| 14:58 | Rollback to v2.3.3 initiated |
| 15:10 | Rollback complete, error rate dropping |
| 15:18 | Service fully recovered, incident resolved |

## Root Cause Analysis

### What Happened

The v2.3.4 deployment included a change to the database query pattern that inadvertently removed connection pooling for a frequently-called endpoint. Each request opened a new database connection instead of reusing pooled connections.

### Why It Happened

1. **Proximate Cause**: Code change in `PaymentRepository.java` replaced pooled `DataSource` with direct `DriverManager.getConnection()` calls.

2. **Contributing Factors**:
   - Code review did not catch the connection handling change
   - No integration tests specifically for connection pool behavior
   - Staging environment has lower traffic, masking the issue
   - Database connection metrics alert threshold was too high (90%)

3. **5 Whys Analysis**:
   - Why did the service fail? → Database connections exhausted
   - Why were connections exhausted? → Each request opened new connection
   - Why did each request open new connection? → Code bypassed connection pool
   - Why did code bypass connection pool? → Developer unfamiliar with codebase patterns
   - Why was developer unfamiliar? → No documentation on connection management patterns

### System Diagram

[Client] → [Load Balancer] → [Payment Service] → [Database] ↓ Connection Pool (broken) ↓ Direct connections (cause)

## Detection

### What Worked
- Error rate alert fired within 8 minutes of deployment
- Grafana dashboard clearly showed connection spike
- On-call response was swift (2 minute acknowledgment)

### What Didn't Work
- Database connection metric alert threshold too high
- No deployment-correlated alerting
- Canary deployment would have caught this earlier

### Detection Gap
The deployment completed at 14:23, but the first alert didn't fire until 14:31 (8 minutes). A deployment-aware alert could have detected the issue faster.

## Response

### What Worked
- On-call engineer quickly identified database as the issue
- Rollback decision was made decisively
- Clear communication in incident channel

### What Could Be Improved
- Took 10 minutes to correlate issue with recent deployment
- Had to manually check deployment history
- Rollback took 12 minutes (could be faster)

## Impact

### Customer Impact
- 12,000 unique customers affected
- Average impact duration: 35 minutes
- 847 support tickets (about 7.1% of the affected-user count; not necessarily unique users)
- Customer satisfaction score dropped 12 points

### Business Impact
- Estimated revenue loss: $45,000
- Support cost: ~$2,500 (agent time)
- Engineering time: ~8 person-hours

### Technical Impact
- Database primary experienced elevated load
- Some replica lag during incident
- No permanent damage to systems

## Lessons Learned

### What Went Well
1. Alerting detected the issue before customer reports
2. Team collaborated effectively under pressure
3. Rollback procedure worked smoothly
4. Communication was clear and timely

### What Went Wrong
1. Code review missed critical change
2. Test coverage gap for connection pooling
3. Staging environment doesn't reflect production traffic
4. Alert thresholds were not tuned properly

### Where We Got Lucky
1. Incident occurred during business hours with full team available
2. Database handled the load without failing completely
3. No other incidents occurred simultaneously

## Action Items

| Priority | Action | Owner | Due Date | Ticket |
|----------|--------|-------|----------|--------|
| P0 | Add integration test for connection pool behavior | @alice | 2024-01-22 | ENG-1234 |
| P0 | Lower database connection alert threshold to 70% | @bob | 2024-01-17 | OPS-567 |
| P1 | Document connection management patterns | @alice | 2024-01-29 | DOC-89 |
| P1 | Implement deployment-correlated alerting | @bob | 2024-02-05 | OPS-568 |
| P2 | Evaluate canary deployment strategy | @charlie | 2024-02-15 | ENG-1235 |
| P2 | Load test staging with production-like traffic | @dave | 2024-02-28 | QA-123 |

## Appendix

### Supporting Data

#### Error Rate Graph
[Link to Grafana dashboard snapshot]

#### Database Connection Graph
[Link to metrics]

### Related Incidents
- 2023-11-02: Similar connection issue in User Service (POSTMORTEM-42)

### References
- Connection Pool Best Practices
- Deployment Runbook

テンプレート2：5つのなぜ分析

# 5 Whys Analysis: [Incident]

## Problem Statement
Payment service experienced 47-minute outage due to database connection exhaustion.

## Analysis

### Why #1: Why did the service fail?
**Answer**: Database connections were exhausted, causing all new requests to fail.

**Evidence**: Metrics showed connection count at 100/100 (max), with 500+ pending requests.

---

### Why #2: Why were database connections exhausted?
**Answer**: Each incoming request opened a new database connection instead of using the connection pool.

**Evidence**: Code diff shows direct `DriverManager.getConnection()` instead of pooled `DataSource`.

---

### Why #3: Why did the code bypass the connection pool?
**Answer**: A developer refactored the repository class and inadvertently changed the connection acquisition method.

**Evidence**: PR #1234 shows the change, made while fixing a different bug.

---

### Why #4: Why wasn't this caught in code review?
**Answer**: The reviewer focused on the functional change (the bug fix) and didn't notice the infrastructure change.

**Evidence**: Review comments only discuss business logic.

---

### Why #5: Why isn't there a safety net for this type of change?
**Answer**: We lack automated tests that verify connection pool behavior and lack documentation about our connection patterns.

**Evidence**: Test suite has no tests for connection handling; wiki has no article on database connections.

## Root Causes Identified

1. **Primary**: Missing automated tests for infrastructure behavior
2. **Secondary**: Insufficient documentation of architectural patterns
3. **Tertiary**: Code review checklist doesn't include infrastructure considerations

## Systemic Improvements

| Root Cause | Improvement | Type |
|------------|-------------|------|
| Missing tests | Add infrastructure behavior tests | Prevention |
| Missing docs | Document connection patterns | Prevention |
| Review gaps | Update review checklist | Detection |
| No canary | Implement canary deployments | Mitigation |

テンプレート3：簡易事後分析（軽微なインシデント向け）

# Quick Postmortem: [Brief Title]

**Date**: 2024-01-15 | **Duration**: 12 min | **Severity**: SEV3

## What Happened
API latency spiked to 5s due to cache miss storm after cache flush.

## Timeline
- 10:00 - Cache flush initiated for config update
- 10:02 - Latency alerts fire
- 10:05 - Identified as cache miss storm
- 10:08 - Enabled cache warming
- 10:12 - Latency normalized

## Root Cause
Full cache flush for minor config update caused thundering herd.

## Fix
- Immediate: Enabled cache warming
- Long-term: Implement partial cache invalidation (ENG-999)

## Lessons
Don't full-flush cache in production; use targeted invalidation.

ファシリテーションガイド

事後分析ミーティングの進行

## Meeting Structure (60 minutes)

### 1. Opening (5 min)
- Remind everyone of blameless culture
- "We're here to learn, not to blame"
- Review meeting norms

### 2. Timeline Review (15 min)
- Walk through events chronologically
- Ask clarifying questions
- Identify gaps in timeline

### 3. Analysis Discussion (20 min)
- What failed?
- Why did it fail?
- What conditions allowed this?
- What would have prevented it?

### 4. Action Items (15 min)
- Brainstorm improvements
- Prioritize by impact and effort
- Assign owners and due dates

### 5. Closing (5 min)
- Summarize key learnings
- Confirm action item owners
- Schedule follow-up if needed

## Facilitation Tips
- Keep discussion on track
- Redirect blame to systems
- Encourage quiet participants
- Document dissenting views
- Time-box tangents

避けるべきアンチパターン

アンチパターン	問題点	より良いアプローチ
責任のなすりつけ合い	学びを妨げます	システムに注目します
浅い分析	再発防止につながりません	寄与した条件を証拠に基づいて追究します。5つのなぜに無理に当てはめないでください
アクション項目がない	時間の無駄になります	必ず具体的な次のステップを設定します
非現実的なアクション	完了できません	達成可能なタスクの範囲に絞ります
フォローアップがない	アクションが忘れられます	チケット管理システムで追跡します

ベストプラクティス

すべきこと

• すぐに始める - 記憶はすぐに薄れます
• 具体的に記述する - 正確な時刻、正確なエラーを記載します
• グラフを含める - 視覚的な証拠を示します
• 担当者を割り当てる - 担当者不在のアクション項目を作りません
• 権限のある対象者と共有する - より広く学びを共有する前に、認証情報、個人データ、機微なインシデントの詳細を削除します

してはいけないこと

• 個人を名指しして非難しない - 決して行いません
• 小さなインシデントを見過ごさない - パターンが明らかになることがあります
• 責任追及の文書にしない - 学びが損なわれます
• 無意味な作業を作らない - アクションには意味があるべきです
• フォローアップを省略しない - アクションが完了したことを確認します

リソース

• Google SRE - 事後分析の文化
• Etsyの責任追及を伴わない事後分析
• PagerDutyの事後分析ガイド

制限事項

• タスクが上記の範囲に明確に一致する場合にのみ、このスキルを使用してください。
• 出力を、環境固有の検証、テスト、専門家によるレビューの代わりとして扱わないでください。
• 必要な入力、権限、安全上の境界、成功基準が不足している場合は、作業を中断して確認してください。