postmortem-writing

模式：认知／提示词驱动 — 没有独立的实用脚本；请在 Agent 上下文中使用。

事后复盘撰写指南

全面介绍如何撰写有效且不归咎个人的事后复盘，推动组织学习并防止事故再次发生。

何时使用此技能

• 开展事故后评审
• 撰写事后复盘文档
• 主持不归咎个人的事后复盘会议
• 找出根本原因和促成因素
• 制定可执行的后续事项
• 建设组织学习文化

核心概念

1. 不归咎个人的文化

归咎个人	不归咎个人
“是谁造成的？”	“哪些条件导致了这个问题？”
“有人犯了错”	“系统允许这个错误发生”
惩罚个人	改进系统
隐瞒信息	分享经验
不敢直言	心理安全

2. 事后复盘触发条件

• SEV1 或 SEV2 事故
• 面向客户的服务中断超过 15 分钟
• 数据丢失或安全事故
• 可能造成严重后果的未遂事件
• 新型故障模式
• 需要采取非常规干预措施的事故

快速入门

事后复盘时间线

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

模板

模板 1：标准事后复盘

# 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 (23% of affected 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](internal-wiki/connection-pools)
- [Deployment Runbook](internal-wiki/deployment-runbook)

模板 2：5 个为什么分析法

# 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 |

模板 3：快速事后复盘（轻微事故）

# 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.

主持指南

主持事后复盘会议

## 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

铁律

1. 务必在事故解决后的 48 小时内撰写事后复盘——记忆会逐渐淡去，超过这个时限后，运行背景信息将永久丢失。
2. 绝不在事后复盘中归咎个人——应关注导致事故发生的系统条件、流程漏洞和工具故障。
3. 务必至少追问 3 层“为什么”来追溯根本原因，再得出结论——直接原因只是表象，而非需要修复的系统性根源。
4. 务必为每个行动项指定明确的负责人、优先级和截止日期——没有负责人或截止日期的行动项永远不会完成。
5. 绝不省略“哪些方面进展顺利”部分——强化积极行为和成功的响应，与修复故障同样重要。

应避免的反模式

反模式	问题	更好的做法
相互指责	阻碍学习	关注系统
分析肤浅	无法防止事故再次发生	连续追问 5 次“为什么”
没有行动项	浪费时间	始终制定明确的后续步骤
行动项不切实际	永远无法完成	将范围限定在可实现的任务内
没有跟进	行动项被遗忘	在工单系统中跟踪

最佳实践

应做事项

• 立即开始——记忆很快会淡去
• 描述具体——提供准确时间和错误信息
• 加入图表——提供可视化证据
• 指定负责人——不要留下无人负责的行动项
• 广泛分享——促进组织学习

不应做事项

• 不要点名羞辱——绝不
• 不要忽略小型事故——它们能揭示规律
• 不要写成追责文档——这会扼杀学习
• 不要制造无意义的工作——行动项应有实际意义
• 不要跳过后续跟进——确认行动项已完成

资源

• Google SRE - 事后复盘文化
• Etsy 的不归咎个人事后复盘
• PagerDuty 事后复盘指南

Memory Protocol（必须遵守）

开始前： 阅读 .claude/context/memory/learnings.md

完成后：

• 新模式 -> .claude/context/memory/learnings.md
• 发现的问题 -> .claude/context/memory/issues.md
• 做出的决定 -> .claude/context/memory/decisions.md

假设工作会被中断：如果未记录在记忆中，就等于从未发生。