Debugging Production Incidents in 2026 — A Senior Engineer's Working Loop
Practical incident debugging: observe → hypothesize → test → narrow. Tools (logs, metrics, traces, profiles), playbooks, and what to avoid mid-incident.
Practical incident debugging: observe → hypothesize → test → narrow. Tools (logs, metrics, traces, profiles), playbooks, and what to avoid mid-incident.
Deploy strategy selection: rolling for default, canary for risk-sensitive, blue/green for stateful / instant cutover. Tools and patterns from production.
Practical load testing: pick the right tool, model real traffic, find capacity ceilings, integrate into CI, and avoid common pitfalls.
Practical Argo Workflows: DAG vs steps, parameters, artifacts, retries, when Argo wins over Airflow / Prefect, and operational realities.
Practical edge compute: where edge actually wins (latency, bot mitigation, A/B), platform limits, cold starts, data locality, and when to stay regional.
Practical observability cost cuts: cardinality discipline, log sampling, trace tail-sampling, retention tiers, and self-hosting tradeoffs.
Practical K8s resource sizing: requests and limits, OOMKill and CPU throttling, VPA / Goldilocks for sizing, QoS classes, and avoiding noisy neighbors.
Practical Docker: multi-stage builds, distroless / Alpine vs slim, BuildKit cache mounts, signing, scanning, and shipping small fast secure images.
Practical SLO design: pick SLIs that matter, set realistic targets, define error budgets, alert on burn rate, and make the budget drive engineering tradeoffs.
IaC tool selection: Terraform’s BSL impact, OpenTofu as the OSS fork, Pulumi for full programming languages, and where each fits in 2026.