Ken Chen's Blog

MongoDB FTDC: The Open Source Overhaul One of the biggest challenges for the MongoDB community has always been the "tool gap." Internal MongoDB engineers have access to powerful, proprietary tools like t2 for visualizing FTDC (Full-Time Diagnostic Data Capture) data. While t2 excels at revealing metric details, its interface is often considered dated. More frustratingly, MongoDB support engineers are constantly asked for the t2 tool by external customers, only to have to decline. The reality is simple: External users and consultants don't have access to this essential internal visibility. For years, my tool Keyhole has helped to bridge this gap, relying on the core module mongo-ftdc . This module has long provided an Assessment Panel view of critical FTDC metrics in high fidelity. However, while mongo-ftdc was functional, it had been sitting idle while MongoDB continued its rapid evolution and scale. My continuous motivation is to promote the successful use of Mongo...

Why You’re Probably Here You’ve probably heard terms like vector search , dot product, cosine similarity , or RAG floating around in AI conversations. Maybe you've seen them in documentation or presentations and thought, “I kind of get it, but not really.” This short guide is here to help -- using simple, practical explanations with everyday examples. No math degree required. If you’re building with AI, curious about modern search, or just want to finally understand what those terms mean, you’re in the right place. By the end of this doc, you’ll know what vectors are, how we compare them, and how those comparisons help AI give better answers using your own data. What Is a Vector (In Plain English)? A vector is just a list of numbers that represents the meaning of some text -- like a sentence, paragraph, or document. For example: “I love dogs” → [0.3, -0.1, 0.9, ..., 0.8] You don’t need to know what the numbers mean -- just that similar sentences produce similar vectors . So: “I ...