6 lines
4.2 KiB
JSON
6 lines
4.2 KiB
JSON
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{"type":"model_change","id":"75e9997a","parentId":null,"timestamp":"2026-06-20T09:32:46.939Z","provider":"opencode-go-openai","modelId":"kimi-k2.7-code"}
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{"type":"thinking_level_change","id":"8ebc97f2","parentId":"75e9997a","timestamp":"2026-06-20T09:32:46.939Z","thinkingLevel":"off"}
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{"type":"message","id":"b0dbe13f","parentId":"8ebc97f2","timestamp":"2026-06-20T09:34:24.181Z","message":{"role":"user","content":[{"type":"text","text":"解释一下cache-aside是什么,除了这种还有什么模式 并比较他们的优缺点"}],"timestamp":1781948064180}}
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{"type":"message","id":"97cdbf8b","parentId":"b0dbe13f","timestamp":"2026-06-20T09:34:37.216Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"User asks in Chinese: explain cache-aside, what other patterns exist, and compare their pros/cons. This is a conceptual Q&A, no code edits. Should answer in Chinese, concise, technical. I can mention萌小芽 maybe, but not necessary. Provide clear structure: cache-aside definition + read/write flow; other patterns: read-through, write-through, write-behind (write-back), refresh-ahead; comparison table; summary guidance on when to use which.","thinkingSignature":"reasoning_content"},{"type":"text","text":"## Cache-Aside（旁路缓存）\n\n应用自己负责与缓存和数据库交互，缓存只是“旁路”存在。\n\n**读流程**\n1. 应用先查缓存，命中则返回。\n2. 未命中则查数据库，写回缓存，再返回。\n\n**写流程**\n1. 应用先写数据库。\n2. 然后删除（或更新）缓存。\n\n**典型场景**：Redis / Memcached + 关系型数据库，手动控制缓存逻辑。\n\n---\n\n## 其他常见缓存模式\n\n| 模式 | 核心思想 | 读/写行为 |\n|---|---|---|\n| **Read-Through** | 应用只与缓存交互 | 缓存未命中时，由缓存层自己从数据库加载并回填 |\n| **Write-Through** | 写操作时同时写缓存和数据库 | 数据先写入缓存，缓存再同步写数据库 |\n| **Write-Behind / Write-Back** | 先写缓存，异步批量写数据库 | 写操作只更新缓存，由后台线程定期/批量刷盘 |\n| **Refresh-Ahead** | 缓存主动在过期前刷新 | 缓存系统预测热点数据，在 TTL 到期前自动重新加载 |\n\n---\n\n## 优缺点对比\n\n| 模式 | 优点 | 缺点 |\n|---|---|---|\n| **Cache-Aside** | 实现简单、灵活；缓存与数据库解耦； failures 不影响数据库直接访问 | 一致性需自己维护；容易出现脏数据、缓存击穿、穿透、雪崩等问题 |\n| **Read-Through** | 应用代码简洁；缓存层封装数据加载逻辑 | 首次未命中有延迟；需缓存支持该能力；调试略复杂 |\n| **Write-Through** | 缓存与数据库强一致；读操作命中率高 | 写延迟增加（要等两次写入）；写吞吐量受数据库限制 |\n| **Write-Behind** | 写性能极高；可批量/异步写库，削峰 | 一致性最弱；掉电/崩溃可能丢数据；实现复杂 |\n| **Refresh-Ahead** | 热点数据几乎无过期抖动；适合读多写少 | 需要预测热点；可能浪费资源刷新冷门数据 |\n\n---\n\n## 怎么选？\n\n- **Cache-Aside**：最常用，业务自己掌控缓存逻辑，适合大多数 Web 应用。\n- **Read-Through**：希望应用不感知数据库细节，配合缓存中间件使用。\n- **Write-Through**：对一致性要求高，且写量不大的场景。\n- **Write-Behind**：高并发写、日志/统计类数据，可容忍短暂不一致。\n- **Refresh-Ahead**：读极多、对延迟敏感的热点数据，如排行榜、配置项。\n\n实际项目中，**Cache-Aside + Read-Through 或 Write-Behind 组合**使用也很常见。"}],"api":"openai-completions","provider":"opencode-go-openai","model":"kimi-k2.7-code","usage":{"input":8464,"output":684,"cacheRead":512,"cacheWrite":0,"totalTokens":9660,"cost":{"input":0.008040799999999999,"output":0.0027359999999999997,"cacheRead":0.00008192,"cacheWrite":0,"total":0.010858719999999999}},"stopReason":"stop","timestamp":1781948064181,"responseId":"chatcmpl-6a365ea145b4c1f3c1c08cce"}}
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