First on-call rotation starts next week. Already dreading it. I've set up the alerting, but never been on the receiving end.
What actually matters? I'm not looking for platitudes.
* How do you filter signal from noise? Our PagerDuty is a firehose.
* What's in your essential runbook template?
* Post-mortem process that doesn't waste everyone's time?
* Any scripts to automate the initial triage? (e.g., checking pod status, service endpoints)
Example of a script I'm thinking of for our K8s alerts:
```bash
#!/bin/bash
# Quick-n-dirty for pod-related alerts
POD_ALERT=$1
kubectl get pods -A | grep -v Running | grep -v Completed
kubectl describe pod $POD_ALERT -n ${NAMESPACE:-default}
kubectl logs --tail=50 $POD_ALERT -n ${NAMESPACE:-default}
```
Is this the right approach? What else should I be automating immediately?
Benchmarks or bust.
Your script's a solid start for initial triage, but you might want to add a quick check for node pressure and pod evictions. Run 'kubectl describe nodes | grep -A 5 -i pressure' to see if it's a broader resource issue before you spend time on a single pod.
Filtering signal from noise is the hardest part. We ended up creating a separate, low-priority notification channel for flaky alerts that were more informational. Anything in the main PagerDuty queue had to be actionable and require immediate intervention, otherwise we'd downgrade it. It took a few rotations to get that right.
What's your process for escalating if you're stuck? Having that decision tree written down, with clear owner contacts, saved me more than any script.
Your script assumes the pod name is the root cause. It often isn't. You'll spend 30 minutes on logs only to find the upstream database is down. Start with service endpoints and external dependencies first.
Filtering noise requires you to ignore the alerting you just set up. Half those PagerDuty rules are wrong. The signal is whatever wakes you up at 3 AM twice in a row.
Runbooks are useless if they're outdated, and they always are. The template should just be a list of three people to call when things break.
Automate the blame, not the triage. Your first script should ping the service owner's Slack channel with the alert details. Let them tell you it's a known issue.
Just saying.
Your script is fine as a first pass, but you're automating the wrong problem. It's like bringing a bucket to a flood because you saw a puddle.
The real firehose filter is time-based. Any alert that hasn't triggered again in 10 minutes is probably just background radiation. If it's truly urgent, it'll page you twice.
Your runbook template needs just two sections: "Who broke it" and "How to wake them up." Everything else is documentation theater.
And automate the post-mortem scheduling, not the triage. A script that opens a Jira ticket with the alert title and timestamps the moment you acknowledge the page. That's the only process that won't waste time, because it forces a paper trail.
Data over dogma.
Your point about checking node pressure before pod details is valid, but that grep is fragile. It won't catch taints, network unavailability, or Kubelet issues. A better initial command is `kubectl get nodes -o wide` and then `kubectl describe node ` for anything with a `NotReady` status. The broader resource issue is often a disk pressure eviction, which that grep will miss if the output format changes.
On escalation, a written decision tree is necessary but insufficient. It decays. We version ours alongside the service code in a repo, and the runbook automation pulls the latest at alert time. If the contact list is older than the last deployment, it flags itself as stale.
Separating channels for flaky alerts only works if you have the organizational discipline to actually fix them. Otherwise, you've just created a graveyard of ignored warnings. The rule we enforce: any alert that fires three times in a low-priority channel must be either fixed, made actionable, or deleted.