System Reliability and Resilience
Reliability is boring, and that is the point. The systems that stay up are not the ones running on heroic late-night recoveries — they are the ones whose operators have made failure cheap, visible, and routine. The articles in this collection treat reliability as a discipline of preventing the dramatic story rather than a craft of telling it afterwards.
A core theme is bounded state. Caches that live forever, connections that never get recycled, queues that grow without a ceiling, and configuration that drifts in long-running processes all share a single property: they postpone failure until it cannot be debugged calmly. Explicit TTLs, capped buffers, and connection lifetimes measured in hours rather than months turn slow leaks into early warnings. The articles cover how to pick those bounds without paying a throughput tax, and what to monitor when the bounds themselves become the bottleneck.
Scheduled restarts are the second recurring theme, and the one most teams resist on principle. A service that has been running for 200 days is not a sign of reliability — it is an unanswered question about whether it can come back up cleanly. Planned restarts force that question every 24 to 72 hours, while the team is awake and the change window is open. Articles cover where this discipline matters most and where it would just add noise.
Startup validation gets specific attention. A process that starts and then fails on its first real request is a process that wasted its rollout window. Fail-fast checks on configuration, database connectivity, downstream identity, and required secrets belong in the boot path. If the system cannot prove it is ready, traffic should not arrive. The pattern is simple; the discipline of keeping the checklist current is the actual work.
Expect direct takes on chaos engineering done well versus chaos engineering done for the conference talk, on health endpoints that lie, on circuit breakers configured to feel good rather than to protect, and on why most “high availability” architectures fail their first real regional event.
AKS Disaster Recovery: Why Your Untested Backup Will Fail
Your cluster will fail. The question is not if, but when, and whether you can recover before customers notice. Most organizations discover their backup strategy does not work during an actual outage, when recovery time matters most and manual heroics cannot save you.
If you run Azure Kubernetes Service (AKS) in production, you need a recovery plan that engineers can execute half asleep at 2 AM. We will go through what to back up, how Velero works in day-to-day operations, when Azure Backup for AKS is enough, and how to design realistic failover with measurable Recovery Time Objective (RTO) and Recovery Point Objective (RPO).
The goal is simple: repeatable recovery procedures you have already tested, not a document that looks good in Confluence but fails during an incident.
Storage Architecture & Stateful Workloads in AKS
AKS Cluster Upgrades: Zero-Downtime Operations That Actually Work
