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Kubernetes' `kubectl get all` command is deceptively named—it only retrieves about 10% of your cluster's resources, leaving you to write complex bash loops and grep pipelines to find everything else. This video tackles the root cause: etcd is a key-value store, not a database, and was never designed for comprehensive querying. The solution? Syncing Kubernetes metadata into a Vector database that enables both traditional queries and semantic search. The demonstration walks through setting up this architecture using Qdrant as the Vector DB, showing how queries that once required painfully slow iterations through hundreds of API resource definitions now return results almost instantly. More importantly, semantic search understands meaning rather than just exact text matches—asking "find all databases" returns StatefulSets, CloudNativePG clusters, Qdrant instances, and AWS RDS resources alike, regardless of naming conventions. The system integrates with AI agents through an MCP server, allowing natural language questions about your cluster while keeping context clean and responses actionable. #Kubernetes #VectorDatabase #DevOps Consider joining the channel: https://www.youtube.com/c/devopstoolkit/join ▬▬▬▬▬▬ 🔗 Additional Info 🔗 ▬▬▬▬▬▬ ➡ Transcript and commands: https://devopstoolkit.live/kubernetes/why-kubernetes-querying-is-broken-and-how-i-fixed-it 🔗 DevOps AI Toolkit: https://devopstoolkit.ai ▬▬▬▬▬▬ 💰 Sponsorships 💰 ▬▬▬▬▬▬ If you are interested in sponsoring this channel, please visit https://devopstoolkit.live/sponsor for more information. Alternatively, feel free to contact me over Twitter or LinkedIn (see below). ▬▬▬▬▬▬ 👋 Contact me 👋 ▬▬▬▬▬▬ ➡ BlueSky: https://vfarcic.bsky.social ➡ LinkedIn: https://www.linkedin.com/in/viktorfarcic/ ▬▬▬▬▬▬ 🚀 Other Channels 🚀 ▬▬▬▬▬▬ 🎤 Podcast: https://www.devopsparadox.com/ 💬 Live streams: https://www.youtube.com/c/DevOpsParadox ▬▬▬▬▬▬ ⏱ Timecodes ⏱ ▬▬▬▬▬▬ 00:00 Kubernetes Query is Broken 01:14 Kubernetes Query Limitations 05:14 Kubernetes Query and Semantic Search 08:27 Kubernetes Query Architecture