Kubernetes Workload Readiness Template
A fill-in readiness record for a workload going onto Kubernetes, probes that mean the right thing, requests and limits from measurement, disruption budgets, secrets, network policy, and what happens during a node failure.
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Kubernetes will keep a badly-configured workload running in a way that looks healthy. Most of the fields below exist to make the difference between "the pod is up" and "the service is working" visible before customers find it.
Workload: _______________ Namespace: _______________ Owner (person): _______________ Date: _______ Cluster / environment: _______________
1. Shape#
| Workload type | Deployment / StatefulSet / Job / CronJob / DaemonSet |
| Replicas (min / max) | |
| Stateless | yes / no, if no, what state and where |
| Can two instances run at once safely | yes / no |
| Startup time to ready |
2. Probes: the three, and what each means#
| Probe | Endpoint | What it checks | Timing |
|---|---|---|---|
| Startup | |||
| Liveness | |||
| Readiness |
- Liveness answers should this container be restarted. It must check only the process itself.
- Readiness answers should this pod receive traffic. It may check dependencies.
- Startup protects a slow-starting application from being killed before it is up.
Pointing liveness at a check that includes a dependency creates a cluster-wide restart storm the moment that dependency is briefly slow. The database hiccups; every pod fails liveness; every pod restarts; the database is now handling a thundering reconnect. Keep them separate.
Slow-start applications: startup probe configured? yes / no (Without one, a long boot looks like a liveness failure and the pod never gets to start.)
3. Resources#
| Request | Limit | Basis | |
|---|---|---|---|
| CPU | measured / guessed | ||
| Memory | measured / guessed |
- Requests set from observed usage: yes / no, date measured: _______
- Memory limit == request (guaranteed class)? yes / no
- Runtime told about the container memory limit: yes / no
A CPU limit throttles; a memory limit kills. Setting memory requests too low gets pods scheduled onto nodes that cannot really hold them; setting them far too high wastes a cluster. Both are corrected by measurement, not by convention.
4. Disruption and scheduling#
| PodDisruptionBudget (minAvailable / maxUnavailable) | |
| Anti-affinity so replicas are not on one node | yes / no |
| Topology spread across zones | |
| Tolerations / node selectors, and why | |
| Priority class |
What happens when a node is drained? _______________ What happens when a node dies without warning? _______________
If both answers are "the replicas move", check that the disruption budget and anti-affinity actually allow it: a single-replica workload with a strict budget blocks node maintenance entirely.
5. Shutdown#
| SIGTERM handled gracefully | yes / no |
| terminationGracePeriodSeconds | |
| preStop hook (e.g. brief sleep to drain endpoints) | |
| In-flight requests completed | yes / no |
There is a genuine race at shutdown: a pod can stop receiving traffic slightly after it starts terminating, because endpoint removal propagates asynchronously. A short preStop delay is the standard mitigation and is worth having wherever dropped requests matter.
6. Configuration and secrets#
| Config source | ConfigMap / env / mounted file |
| Secrets source | |
| Secrets encrypted at rest in etcd | yes / no |
| Rotation picked up without a redeploy | yes / no |
| No secrets in the manifest or in git | confirmed |
7. Security context#
| runAsNonRoot | yes / no |
| Read-only root filesystem | yes / no |
| Capabilities dropped | |
| allowPrivilegeEscalation | |
| Service account: dedicated, minimal RBAC | yes / no |
| Automount of the service account token needed | yes / no |
8. Network#
| NetworkPolicy applied (default deny?) | |
| Ingress path, TLS termination | |
| Egress required to | |
| Service type |
By default, everything in a cluster can talk to everything. A namespace with no network policy is a flat network, and the first lateral movement in an incident goes wherever it likes.
9. Observability and operations#
- [ ] Logs to stdout, collected centrally
- [ ] Metrics scraped, dashboard exists
- [ ] Alerts route to a human who can act
- [ ] HPA configured on a metric that reflects real load
- [ ] Runbook for the three most likely failures
- [ ] Someone would notice if the workload were scaled to zero
10. Before production#
- [ ] Rolling update tested, no dropped requests observed
- [ ] Pod killed deliberately; recovery verified
- [ ] Node drained deliberately; behaviour verified
- [ ] Resource limits verified under load, not at rest
- [ ] Rollback of a bad image tested
Sign-off#
| Name | Date | |
|---|---|---|
| Built by | ||
| Platform review | ||
| Approved for production |