发布时间：2026-01-05 人气：
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：
当下止血
Retry-After。中期方案（能抗开售洪峰）
Idempotency-Key；支付回调可重试且幂等。公平与反黄牛
压测与 SLO
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？
