kubernetes-deployment-patterns
Kubernetes deployment strategies and workload patterns for production-grade applications. Use when deploying to Kubernetes, implementing rollout strategies, or designing cloud-native application architectures.
Best use case
kubernetes-deployment-patterns is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Kubernetes deployment strategies and workload patterns for production-grade applications. Use when deploying to Kubernetes, implementing rollout strategies, or designing cloud-native application architectures.
Teams using kubernetes-deployment-patterns should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/kubernetes-deployment-patterns/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How kubernetes-deployment-patterns Compares
| Feature / Agent | kubernetes-deployment-patterns | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Kubernetes deployment strategies and workload patterns for production-grade applications. Use when deploying to Kubernetes, implementing rollout strategies, or designing cloud-native application architectures.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Kubernetes Deployment Patterns
Expert guidance for production-grade Kubernetes deployments covering deployment strategies, workload types, configuration management, resource optimization, and autoscaling patterns for cloud-native applications.
## When to Use This Skill
- Implementing deployment strategies (rolling updates, blue-green, canary releases)
- Choosing appropriate workload types (Deployment, StatefulSet, DaemonSet, Job)
- Designing rollout strategies for zero-downtime deployments
- Implementing configuration management with ConfigMaps and Secrets
- Setting up resource management and autoscaling (HPA, VPA)
- Configuring health checks and probe strategies
- Designing highly available applications on Kubernetes
- Implementing batch processing and scheduled jobs
## Core Concepts
### Deployment Strategies
**Rolling Update:** Gradually replace old pods with new ones (zero-downtime, default)
**Recreate:** Terminate all old pods before creating new ones (brief downtime)
**Blue-Green:** Run two environments, switch traffic instantly (2x resources)
**Canary:** Gradually shift traffic to new version while monitoring (risk mitigation)
### Workload Types
**Deployment:** Stateless applications (web servers, APIs, microservices)
**StatefulSet:** Stateful applications (databases, message queues)
**DaemonSet:** Node-level services (log collectors, monitoring agents)
**Job:** One-time tasks (batch processing, migrations)
**CronJob:** Scheduled tasks (backups, periodic reports)
### Resource Management
**Requests:** Guaranteed resources for scheduling
**Limits:** Maximum resources enforced by kubelet
**HPA:** Horizontal Pod Autoscaler (scale replicas based on metrics)
**VPA:** Vertical Pod Autoscaler (adjust resource requests/limits)
## Quick Reference
| Task | Load reference |
| --- | --- |
| Deployment strategies (rolling, blue-green, canary) | `skills/kubernetes-deployment-patterns/references/deployment-strategies.md` |
| Workload types (Deployment, StatefulSet, DaemonSet, Job) | `skills/kubernetes-deployment-patterns/references/workload-types.md` |
| Configuration management (ConfigMaps, Secrets) | `skills/kubernetes-deployment-patterns/references/configuration-management.md` |
| Resource management and autoscaling (HPA, VPA) | `skills/kubernetes-deployment-patterns/references/resource-management.md` |
| Production best practices and security | `skills/kubernetes-deployment-patterns/references/production-best-practices.md` |
## Workflow
### 1. Choose Deployment Strategy
```yaml
# Rolling update for standard deployments
strategy:
type: RollingUpdate
rollingUpdate:
maxSurge: 1
maxUnavailable: 0
# Recreate for incompatible versions
strategy:
type: Recreate
```
### 2. Select Workload Type
- **Stateless?** → Use Deployment
- **Stateful with persistent identity?** → Use StatefulSet
- **One pod per node?** → Use DaemonSet
- **Run to completion?** → Use Job
- **Run on schedule?** → Use CronJob
### 3. Configure Resources
```yaml
resources:
requests:
memory: "256Mi"
cpu: "250m"
limits:
memory: "512Mi"
cpu: "1000m"
```
### 4. Add Configuration
```yaml
# ConfigMap for non-sensitive config
envFrom:
- configMapRef:
name: app-config
# Secret for sensitive data
env:
- name: DB_PASSWORD
valueFrom:
secretKeyRef:
name: db-credentials
key: password
```
### 5. Implement Health Checks
```yaml
livenessProbe:
httpGet:
path: /healthz
port: 8080
initialDelaySeconds: 30
periodSeconds: 10
readinessProbe:
httpGet:
path: /ready
port: 8080
initialDelaySeconds: 5
periodSeconds: 5
```
### 6. Enable Autoscaling
```yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
spec:
scaleTargetRef:
kind: Deployment
name: app
minReplicas: 2
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70
```
## Common Mistakes
1. **Using `latest` tag:** Always use specific version tags for reproducibility
2. **No resource limits:** Can cause resource starvation and cluster instability
3. **Missing health checks:** Kubernetes can't manage pod health without probes
4. **Single replica in production:** No high availability or resilience
5. **Secrets in ConfigMaps:** Use Secrets for sensitive data, not ConfigMaps
6. **No update strategy:** Leads to unpredictable deployment behavior
7. **Running as root:** Security vulnerability, violates least privilege
8. **No monitoring:** Can't detect or debug issues in production
## Resources
- **Official Docs:** https://kubernetes.io/docs/concepts/workloads/
- **Deployment Strategies:** https://kubernetes.io/docs/concepts/workloads/controllers/deployment/
- **StatefulSets:** https://kubernetes.io/docs/concepts/workloads/controllers/statefulset/
- **Autoscaling:** https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/
- **Configuration:** https://kubernetes.io/docs/concepts/configuration/
- **Best Practices:** https://kubernetes.io/docs/concepts/configuration/overview/