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的写放大模型、容量选择和动态策略，并结合实验讨论其进入生产系统时面临的目标函数与负载建模限制。","feed":"https://rss.csdn.net/weixin_43705457/rss/map","tag":"2018","title":"问津集 #19：Separation or Not——乱序数据分流的写放大建模与策略选择"},{"_id":"6a987ffff998eec1b324dc57","link":"https://blog.csdn.net/weixin_43705457/article/details/164304537","__v":0,"author":"李兆龙的博客","createdAt":"2026-09-02T19:58:55.366Z","date":"2026-09-02T13:17:52.000Z","description":"这里的 Workload 指一组共同决定产品形态的负载与业务约束：数据由谁产生，怎样进入，Schema 如何演化，写入峰值多高，默认查询扫多少时间，哪些结果需要预计算，调查过程中会连续发出多少次查询，最后又由哪一类人为结果付费。同一个 ClickHouse Engine，跑 Infra Observability、LLM Observability 和 Security Analytics，产品形","feed":"https://rss.csdn.net/weixin_43705457/rss/map","tag":"2018","title":"从一到无穷大 #86：ClickHouse 收购 RunReveal——可观测性竞争进入宏观调控阶段"},{"_id":"6a980f7ff998eec1b324dc56","link":"https://blog.csdn.net/weixin_43705457/article/details/164303295","__v":0,"author":"李兆龙的博客","createdAt":"2026-09-02T11:58:55.544Z","date":"2026-09-02T11:33:04.000Z","description":"TS-LSM-Tree 不算是 Timon 的创新。去掉名字以后，它的外壳就是 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GC"},{"_id":"6a956c88f998eec1b324dc54","link":"https://blog.csdn.net/liushengxi_root/article/details/163834186","__v":0,"author":"Tattoo的博客","createdAt":"2026-08-31T11:59:04.456Z","date":"2026-08-31T07:41:01.000Z","description":"我们只需要根据内容生成摘要，≤150字。内容是关于K8s基础概念和原理，按生命周期串讲。摘要应概括核心：镜像容器、Pod、节点、控制器、Service、Ingress、ConfigMap/Secret、PV/PVC、Namespace，以及部署流程和思维转变。注意字数限制。 本文按应用从代码到服务的生命周期，串联K8s核心概念：镜像与容器、Pod（最小部署单元）、节点（控制/工作）、各类控制器（D","feed":"https://rss.csdn.net/liushengxi_root/rss/map","tag":"2016","title":"K8S 基础概念和常用原理"},{"_id":"6a941b00f998eec1b324dc53","link":"https://blog.csdn.net/weixin_43705457/article/details/164191583","__v":0,"author":"李兆龙的博客","createdAt":"2026-08-30T11:58:56.214Z","date":"2026-08-30T09:56:28.000Z","description":"RangeReduce 最直接的优势是复用 Range Query 已经完成的 I/O、k-way merge、旧版本去重和 Tombstone 过滤。将这批有效 Entry 写入更深层以后，后续 Compaction 可以少读、少归并一部分数据，相同或重叠范围的查询也能少访问一些 Run。长 Range Query、查询范围反复重叠，以及更新和删除较多的负载，更容易覆盖本次写回成本。[1]它的代","feed":"https://rss.csdn.net/weixin_43705457/rss/map","tag":"2018","title":"问津集 #15：RangeReduce——查询驱动 Compaction 收益"},{"_id":"6a941b00f998eec1b324dc52","link":"https://blog.csdn.net/weixin_43705457/article/details/164187777","__v":0,"author":"李兆龙的博客","createdAt":"2026-08-30T11:58:56.214Z","date":"2026-08-30T04:22:34.000Z","description":"一个 Agent 卡在缺失的蛋白质数据库文件上。不同训练任务之间没有通信工具，它便在 OpenAI 内部的 Artifactory 里留下一句话：[1]谁有这个文件，上传一下。一个 Agent 控制了客户托管在 Modal 上的 CyberGym 应用，随即向留言板更新进展：[2]发现 Modal 应用内的远程代码执行能力。OpenAI 没有披露下一条留言的精确时间；按官方叙述顺序，它出现在 7 ","feed":"https://rss.csdn.net/weixin_43705457/rss/map","tag":"2018","title":"从一到无穷大 #85：OpenAI × Hugging Face——一条留言如何变成 700 个 Agent 的攻击网络"},{"_id":"6a9339c8f998eec1b324dc51","link":"https://blog.csdn.net/yelubin612/article/details/164172603","__v":0,"author":"yelubin612的博客","createdAt":"2026-08-29T19:58:00.173Z","date":"2026-08-29T13:29:09.000Z","description":"两个原因：一是保证最后一个 ACK 能被对方收到，丢了的话对方会重发 FIN，2MSL 够它重发一次再处理完；二是让这次连接的旧报文在网络里彻底消失，别干扰下一次用相同四元组建立的新连接。","feed":"https://rss.csdn.net/yelubin612/rss/map","tag":"2025","title":"Chatroom问题复盘&答辩Later"},{"_id":"6a92c97ff998eec1b324dc50","link":"https://blog.csdn.net/weixin_43705457/article/details/164172037","__v":0,"author":"李兆龙的博客","createdAt":"2026-08-29T11:58:55.123Z","date":"2026-08-29T09:59:34.000Z","description":"这套架构适合同时满足几个条件的系统：大量 LSM 实例共享远端存储，实例之间的 Compaction 强度不均匀，存储附近可以部署计算资源，而且 Compaction 输入能够被封装成独立任务。单机 NVMe、实例数量很少、顺序写以 Trivial Move 为主时，共享控制面提供不了多少收益。论文自己的顺序读写实验也是这个结果。[1]我的重点在于调度。一个 Compaction Task 默认需","feed":"https://rss.csdn.net/weixin_43705457/rss/map","tag":"2018","title":"问津集 #14：CaaS-LSM——把 Compaction 变成存储侧共享服务"},{"_id":"6a92c948f998eec1b324dc4f","link":"https://blog.csdn.net/2503_93757505/article/details/164168812","__v":0,"author":"2503_93757505的博客","createdAt":"2026-08-29T11:58:00.138Z","date":"2026-08-29T05:35:23.000Z","description":"问题根因核心优化消息慢每条消息同步写 MySQL，每次提交 fsync；连接池只省了握手Redis + 攒批回写 MySQL；在线跳过离线表；文件慢停等协议，每个 chunk 等一个 RTT；read/write 两次拷贝停等 → 滑动窗口 + 累积 ACK；read/write → sendfile 零拷贝事务事务绑定连接、每次提交 fsync用 RAII 管连接，避免长事务和泄漏。","feed":"https://rss.csdn.net/2503_93757505/rss/map","tag":"2025","title":"chatroom问题与优化"},{"_id":"6a917801f998eec1b324dc4e","link":"https://blog.csdn.net/weixin_43705457/article/details/164150432","__v":0,"author":"李兆龙的博客","createdAt":"2026-08-28T11:58:57.057Z","date":"2026-08-28T09:35:47.000Z","description":"我认为 FlexEngine 很优雅，原因是这套设计几乎顺着工程问题自然长出来。假设一个 Pod 承载 2000 个数据分片，删除、Checkpoint、正排与倒排索引的 Compaction 都可以直接向存储发起请求，前台读写迟早会被拖住。到了这个规模，后台任务不能再各自为政，精细化调度是必需品。上面的 2000 个分片及索引任务来自我对一般存储系统的延伸，论文没有披露这些部署细节。文中每个租户","feed":"https://rss.csdn.net/weixin_43705457/rss/map","tag":"2018","title":"问津集 #13：FlexEngine—多租户的精细化 I/O 控制"},{"_id":"6a909700f998eec1b324dc4d","link":"https://blog.csdn.net/weixin_43705457/article/details/164125920","__v":0,"author":"李兆龙的博客","createdAt":"2026-08-27T19:58:56.081Z","date":"2026-08-27T13:51:36.000Z","description":"2026 年 8 月 26 日，AWS 与 DuckLabs 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