Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
Most SLI dashboards track things nobody notices. Here's how we picked the handful of signals that map to real user pain, and dropped the vanity metrics.
The dashboard said the database was fine. It wasn't. Here's how pg_stat_statements found the query eating 40% of our Postgres CPU.
Free memory is a lie and load average doesn't see memory stalls. How Pressure Stall Information gives you a direct, early signal of memory contention — and how we wired it into alerts and autoscaling.
Cause-based alerts page you for things that don't matter and miss things that do. How we rebuilt alerting around SLO burn rates — multi-window, multi-burn-rate — and cut pages while catching more real pain.
The "three pillars" framing misses the point — what matters is correlating across them. The patterns that earn their place and the tooling decisions that pay back.
Production monitoring catches user-facing issues. CI failures stay invisible until someone notices the merge queue is stuck. The metrics and alerts that make pipelines observable.
Static thresholds on error rate produce noisy alerts. Burn-rate alerting flips the question to "are we burning the error budget faster than we can sustain?" — and pages only on real problems.
Wrong SLI metrics mean green dashboards while users churn. The discipline of picking signals that move with what users actually feel, and the ones that look reliable but lie.
Three layers of pooling, three different jobs. We learned the hard way which to use when. Real numbers from a 8k-connection workload.
We deployed the same edge function on both platforms and measured for a quarter. Where each wins, where each loses, and the surprises along the way.
We started using eBPF tooling for ad-hoc production debugging six months ago. Three real incidents where it cut investigation time from hours to minutes.
A two-line config change to an Argo Rollouts analysis template caught a regression that would have cost ~$40k in API spend before we noticed. Here's the pattern.
