Renjie Zhu

Put down my thoughts on my iPad and took a deep dive into append-only logs and hash indexes, two foundational ideas behind storage engines and databases. Starting from first principles, I explore why append-only designs are preferred and how hash indexes are built on top of logs. From there, I show where hash indexes fall short and set the stage for what comes next. In the follow-up video, I build on this foundation by diving into SSTables and LSM Trees, breaking down how they work internally and why they’re so effective in real-world database systems. YT video link for append-only logs & Hash indexes - https://lnkd.in/gfT4XWDR YT video link for SSTables & LSM trees - https://lnkd.in/g8pkJJPM

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Last Tuesday, Deliveroo's rider-switch Rails endpoint spiked to 4s latency and intermittent 503s. The flame graphs told the real story and became our speed playbook. Technical context: Flame graphs visualize time across call stacks using sampling profilers. In Rails, latency often stems from DB calls, serialization, or middleware. This makes root causes visible to newcomers. Recent tech context includes AI agents and RAG patterns (LangGraph, Claude 3.5, GPT-4o, Gemini 2.0) guiding performance analyses. KEY INSIGHTS: 🔍 Flame graphs reveal hot stacks and where wall time sits. ⚡ Latency on the hot path fell from 4s to ~800ms after fixes. ───────────────────────── 🔗 Read the full article: https://lnkd.in/ghcREzpZ 🎯 Practice interview questions: https://lnkd.in/gmy5drNw #performancetesting #cpuprofiling #memoryprofiling #flamegraphs #latency

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𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗲𝘀𝘀𝗼𝗻 𝗰𝗼𝘀𝘁 𝗼𝘂𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 $𝟲𝟬𝟬 𝗮𝗻𝗱 𝗮 𝗺𝗮𝗿𝗿𝗶𝗮𝗴𝗲 𝗮𝗿𝗴𝘂𝗺𝗲𝗻𝘁. Our VP of Engineering, Xiaofan(James) Luan, was supposed to buy his wife a Dior bag for their anniversary.  Instead, he bought three Claude Code subscriptions and spent the holiday trying to cross-compile 2 million lines of C++. Every fix on one platform broke two others.  $600 later, the only output was "git reset --hard" — and a very cold dinner table.😂 "Make it compile on Windows" is a trap. The real goal was "compile everywhere without hacks" — no AI is going to figure that out for you at 2 am. What worked: constraints before code, review tests not code, bottom-up, one layer at a time. Same task, two days. Then he ran six parallel Claude sessions across three machines with git worktree. The bottleneck stopped being intelligence and started being how fast one person can alt-tab. AI solves exactly the problem you give it. Engineering is in knowing which one to give. His wife is still waiting for that bag. Full story: https://lnkd.in/gtsW_Wvk ——— Follow Milvus, created by Zilliz, for everything related to unstructured data

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If you are in the Bay Area and run Growth or GTM you won't want to miss this 👀

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Over the past year, my team and I built Pinterest's Model Context Protocol (MCP) ecosystem from the ground up: a central registry, a growing fleet of domain-specific MCP servers, and production integrations across our IDEs, internal chat surfaces, and AI agents. MCP is an open standard that gives large language models a unified way to talk to tools and data sources. At Pinterest, we used it as the substrate for AI agents that can safely automate real engineering tasks, from querying Presto data on demand to diagnosing Spark job failures and surfacing institutional knowledge. A few things I'm particularly proud of: * We designed for security from day one: two-layer auth (end-user JWTs + mesh identities), business-group-based access gating, and mandatory human-in-the-loop for sensitive actions. * We made it easy for any team to ship a new MCP server by creating a unified deployment pipeline that handles all the infrastructure, so domain experts just write business logic. * The ecosystem has scaled to 66,000+ invocations per month across 844 monthly active users, saving an estimated 7,000 engineering hours per month. This was a cross-functional effort. Thank you to everyone on the Agent Foundations, Security Engineering, and Traffic Engineering teams who made this possible, and to our engineering sponsors for their support and guidance. Full write-up on the Pinterest Engineering Blog: https://lnkd.in/e-mH26Bw

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