Part 5: GitOps with ArgoCD
Building a Production-Grade Kubernetes Homelab - Part 5 of 8
Building, breaking, and building again.
Insights on DevOps, infrastructure, and modern engineering
Building a Production-Grade Kubernetes Homelab - Part 5 of 8
Building a Production-Grade Kubernetes Homelab - Part 4 of 8
Building a Production-Grade Kubernetes Homelab - Part 3 of 8
Building a Production-Grade Kubernetes Homelab - Part 2 of 8
Building a Production-Grade Kubernetes Homelab - Part 1 of 8
Load balancing at scale requires more than engineering intuition. When Google introduced Maglev, they presented an elegant solution to a fundamental distributed systems problem. Today, this algorithm powers critical infrastructure through implementations like Cilium's eBPF-based load balancer.
While Maglev provides O(1) lookups with near-uniform load and low disruption, its mathematical structure invites deeper algebraic analysis. This article explores advanced theoretical perspectives on Maglev through the lens of group theory, Galois fields, and modern cryptography.
If you've implemented SLO-based alerting, you've likely encountered these seemingly arbitrary numbers: burn rates of 14.4× and 6×, paired with windows of 1h/5m and 6h/30m respectively. The Google SRE Workbook presents these as proven values, but why these specific numbers? Where do they come from?
"In production you don't rise to the level of your architecture... you fall to the level of your observability." My SigNoz dashboards went blood-red: Longhorn's instance-manager pods were thrashing in a tight restart loop, chewing through crash-backoff timers like popcorn. Within minutes I was
When the power went out for the third time in two months, I realized I needed a better disaster recovery strategy. Cloud providers don't lose power, but they do charge by the minute. The solution? A hybrid mesh that runs on-premise by default but automatically bursts into the cloud during outages.
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