Suggesting incident response steps
First, I should focus on immediate incident response, like enabling a waiting room and reducing functionality. I'll consider disabling new sessions, caching read endpoints, and implementing rate limiting for bot mitigation. It’s essential to have a static status page and feature flags ready for degradation.
Next, I'll need to plan for a postmortem, collecting relevant metrics. Then, I'll explore architecture improvements, such as using a pre-queue with a token bucket and a write-optimized purchase flow.Outlining system architecture improvements
I’m considering using per-match partitioning along with idempotency keys for purchases to enhance reliability. Payment orchestration could benefit from an outbox and saga pattern, and I'll implement asynchronous order confirmation using tools like Kafka, SQS, or NATS.
I think employing CQRS for read replication and caching is a good idea. I'll use CDNs for static assets and dynamic caching while addressing bot mitigation through options like proof-of-work and hCaptcha. Assigning fairness through virtual queues and auto-scaling on request per second seems important too. More importantly, I’ll need to focus on observability with detailed metrics and tracing.Crafting action steps
I’m thinking about how to ask the user what they need help with, like writing an incident announcement, developing an emergency technical response plan, designing architecture for high demand, or creating a review checklist and load testing script.
For immediate actions, I’ll implement a virtual waiting room, set capacity thresholds, and utilize a CDN with read-only caching while limiting unnecessary features. It’ll be crucial to manage traffic with strict bottlenecks, WAF, and bot verification, along with caching and delaying order confirmations for smoother operations.
I’ll also focus on observability metrics like RPS and error rates.Providing reservation script
I’m crafting a Lua script for inventory management, outlining the essentials like SKU, quantity, hold time, and order ID.
The script checks if the current stock is sufficient and decrements the stock if it is. It also sets a hold on the inventory for the specified time if there's enough quantity available. I need to think about what to do if the SET NX fails; using a unique order ID should help.
I'll include a brief call example in pseudo Go or Node, keeping everything concise and clear.
这是在说新闻，还是你在现场救火？我可以帮你：
- 写一份对外公告/客服话术
- 制定立即止血的应急操作单
- 设计能扛开售洪峰的架构方案
- 复盘清单与压测脚本雏形 回复数字即可，先给你一版可直接用的技术止血+后续改造建议。
当下止血
- 虚拟候场/排队：在入口前置队列，设置总并发上限；队列页静态化；溢出返回 503+
Retry-After。 - 强缓存+只读降级：CDN 缓存赛事/座位只读接口；临时关闭搜索、历史订单等非关键功能。
- 严格限流与熔断：按 IP/账号/设备指纹限速；对下单、锁票、支付入口设置令牌桶；下游超时快速失败。
- 反刷与风控：WAF 规则提级、人机验证；黑名单/异常指纹限流；放量分批。
- 运营沟通：状态页实时更新、预计恢复时间、补偿政策；在队列页展示排队位置与预计等待。
中期方案（能抗开售洪峰）
- 前置排队/令牌：发放带签名和 TTL 的排队令牌；令牌与账号、设备绑定，防并发绕过。
- 库存服务解耦：单独“库存/占座”服务，按比赛/看台分片；使用 Redis 原子扣减+短时占座（例如 2–5 分钟），超时自动回补。
- 幂等与去重：下单/支付均使用
Idempotency-Key；支付回调可重试且幂等。 - 异步化下单：下单请求入队（Kafka/SQS），后台完成校验与扣减；使用 Saga/Outbox 确保最终一致。
- CQRS 与读扩展：读写分离，读多页面（列表、座位图）强缓存/副本库；写路径保持最短链路。
- 水平扩展：按队列深度+P95 延迟+CPU 做 HPA；预热实例与连接池；热点 Key 分片（座位段/票档）。
公平与反黄牛
- 机制：预约/抽签购、分批次放量、每账号限购，队列随机化而非先到先得。
- 技术：设备指纹+速率限制+行为模型；必要时轻量 PoW/验证码在开售窗口启用。
压测与 SLO
- 目标：峰值 RPS、错误率<1%、下单 P95 延迟、队列可见等待≤N 分钟。
- 工具：k6/Gatling 复现“候场→放量→峰值→尾流”；分别压库存原子性、下单幂等、支付回调重试。
Redis 原子占座示例（Lua） 用于“检查库存→扣减→创建占座”一步到位，防超卖与竞态。
-- KEYS[1]=stock_hash (sku->remain), KEYS[2]=hold_key_prefix
-- ARGV[1]=sku, ARGV[2]=qty, ARGV[3]=hold_ms_ttl, ARGV[4]=order_id
local sku=ARGV[1]; local qty=tonumber(ARGV[2]); local ttl=tonumber(ARGV[3]); local oid=ARGV[4]
local stock=tonumber(redis.call('HGET', KEYS[1], sku) or '0')
if stock < qty then return {err="INSUFFICIENT"} end
redis.call('HINCRBY', KEYS[1], sku, -qty)
local holdKey = KEYS[2] .. ":" .. oid
redis.call('HSET', holdKey, sku, qty)
redis.call('PEXPIRE', holdKey, ttl) -- 到期自动回补由定时任务处理
return {"OK", stock - qty}
回补策略：定时任务扫描过期占座，将数量 HINCRBY 回 stock_hash；订单支付成功后将占座转为已售并记录不可撤销流水。
需要我把上述应急操作写成值班手册，或者给你一套最小实现的接口定义和 k6 压测脚本吗？