Observed Signal · Jul 15, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Neutral
12 DevOps Errors That Page Teams Most
The article catalogs twelve common production DevOps errors that most frequently trigger on-call pages and gives the single first diagnostic check to run for each. Examples include CrashLoopBackOff, ImagePullBackOff/ErrImagePull, OOMKilled (exit code 137), inode exhaustion, DNS timeouts inside pods, Postgres 'too many clients', connection refused, TLS handshake timeouts, read-only filesystems, Multi-Attach volume errors, 502 Bad Gateway, and exec format errors. The author emphasizes that these messages are symptoms, not root causes, and that the key skill is knowing the single command or check that turns a symptom into a cause. The post links to a fuller, searchable library of troubleshooting guides on the author's site for deeper diagnostics and prevention checklists.
Practical operational guidance useful to engineering teams to reduce incidents; valuable but not specific to AdTech nor industry-shifting.
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Key Takeaways & Evidence Grounding
- Author catalogued production DevOps error messages and identified the twelve errors that produce the majority of on-call pages.
- Each error entry includes one prioritized diagnostic check (e.g., 'kubectl logs <pod> --previous' for CrashLoopBackOff).
- The list of twelve errors: CrashLoopBackOff; ImagePullBackOff / ErrImagePull; OOMKilled (exit code 137); No space left on device; DNS timeouts inside pods; Postgres 'FATAL: sorry, too many clients already'; Connection refused; TLS handshake timeout; Read-only file system; Multi-Attach error for volume; 502 Bad Gateway; exec format error.
- Pattern observed: error messages generally describe system symptoms rather than root causes; resolving incidents requires running the correct diagnostic command to reveal the cause.
Connected Companies & Entities
1 Entity mapped“You built an image for one CPU architecture and ran it on another (hello, Apple Silicon → x86 clusters)....”
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Practical Kubernetes Production Troubleshooting Workflow
A practical how-to describing a repeatable sequence for troubleshooting Kubernetes workloads in production. The author prescribes a baseline flow (kubectl get pods -A → kubectl describe pod → kubectl logs --previous → kubectl top → kubectl get events) and a failure-classification approach that maps observed symptoms to targeted diagnosis and recovery actions. Five common scenarios are documented: ImagePullBackOff, CrashLoopBackOff, Pending pods, ingress 502/503 with healthy pods, and cluster DNS/CoreDNS failures. For each scenario the post lists typical root causes, concrete kubectl commands for diagnosis and recovery, and prevention tactics (CI image pinning, startup probes, capacity planning, smoke tests). The guidance emphasizes reading events and previous logs before restarting to preserve crash context.
5 Checks Before Deploying a New Website
A web developer shares a five-item pre-launch checklist built from repeated deployment mistakes: verify redirects and URLs, confirm technical SEO basics, test all important forms in production, audit performance (including Core Web Vitals), and double-check analytics/tracking. The author illustrates common failures—silent form errors, staging canonical tags leaking to production, robots.txt blocking crawlers, and URL-structure changes that caused an ~40% organic traffic drop in one redesign—and recommends manual, production-time verification (including Google Analytics, Tag Manager and Search Console) and a written checklist to avoid weeks of lost visibility or irrecoverable analytics gaps.
10 Next.js Performance Mistakes Slowing Production
This technical guide lists ten common performance mistakes that cause Next.js applications to slow in production and provides fixes and best practices. It emphasizes preferring Server Components over indiscriminate "use client" usage, fetching data on the server to avoid client-side waterfalls, using next/image and next/font for optimized image and font delivery, and choosing static generation or ISR over unnecessary per-request server rendering. The article also recommends avoiding default no-store caching, dynamically loading large client libraries, memoizing expensive client-side work where necessary, using Suspense boundaries for slow data, and regularly measuring real production metrics (bundle sizes, Lighthouse, Core Web Vitals, bundle-analyzer). The guidance is framed to reduce hydration cost, network overhead, layout shift, and backend load.
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