ArgoCD Reconciliation - Milvus Performance Tuning
Details
January 29th at 6pm at Capital Factory
We will meet in the Apollo Room on the 1st Floor! Our schedule is as follows:
6:00-6:30 - Food/Social!
6:30-7:15 - ArgoCD Reconciliation
7:15-8:00 - Milvus Performance Tuning
Please RSVP at
https://community.cncf.io/events/details/cncf-kubernetes-austin-presents-argocd-reconciliation-milvus-performance-tuning/
ATTENTION: WE KNOW PARKING IN DOWNTOWN IS TRICKY!
So, you can park in the building garage for just $8.00 (validation parking tickets will be distributed)!
Street parking will still be an option. More information on parking here: https://www.capitalfactory.com/parking/
Please RSVP here, and please make sure you follow our official social media platforms and stay tuned with the latest news about Kubernetes Austin:
https://linkedin.com/company/k8sAustin
https://instagram.com/k8sAustin
https://x.com/k8sAustin
Everyone is invited to join! We hope to bring the latest news about the Kubernetes and CNCF projects. We had a last-minute speaker change; instead of K8s security, the second slot will be about Optimizing DNS Queries.
Descriptions
Vishwa Gandhi
ArgoCD Under the Hood (GitOps Reconciliation)
Configuration drift is one of the most common causes of instability in Kubernetes. In this talk, she will explain what actually happens after a commit is pushed to Git and how ArgoCD continuously reconciles the cluster back to the desired state. She will break down the reconciliation loop and the roles of key ArgoCD components like the Application Controller, Repo Server, and API Server—so you can build a strong mental model to debug issues and run GitOps confidently in production.
Raghu Shankar
Cloud-Native Vector Databases with Milvus (Benchmarking + Tuning + Observability)
Vector databases are becoming critical for fast similarity search in LLM apps, recommendations, genomics, and more—often at massive scale. This session introduces Milvus, a cloud-native vector database, and shares real benchmarking results from a CNCF-based 2-node Kubernetes cluster (40 cores, 64GB RAM). He will cover key tuning parameters (m, ef, ef_construction), how they impact latency/recall/throughput, and how performance correlates with observability metrics using Prometheus and Grafana.
More information: k8saustin.com