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---
name: gcp-gke
compatibility: opencode
completeness: 95
content-types:
- guidance
- examples
- do-dont
- config
description: '"Provides Managed Kubernetes cluster with automatic scaling, networking,
and GCP service integration"'
license: MIT
maturity: stable
metadata:
domain: cncf
output-format: manifests
related-skills: gcp-autoscaling, gcp-cloud-monitoring, gcp-container-registry, gcp-vpc
role: reference
scope: infrastructure
triggers: gke, kubernetes, container orchestration, k8s, managed kubernetes
archetypes:
- educational
- strategic
anti_triggers:
- brainstorming
- vague ideation
- non-containerized architecture
response_profile:
verbosity: medium
directive_strength: low
abstraction_level: strategic
version: "1.0.0"
---
# Google Kubernetes Engine
Deploy and manage google kubernetes engine infrastructure as part of your cloud-native environment.
## TL;DR Checklist
- [ ] Enable monitoring and logging
- [ ] Configure security and access controls
- [ ] Set up automated backups
- [ ] Enable high availability
- [ ] Implement disaster recovery
- [ ] Document configuration
- [ ] Test failover procedures
- [ ] Set up alerting
---
## When to Use
Use google kubernetes engine when:
- Running cloud infrastructure workloads
- Requiring managed services from the platform
- Needing automatic scaling and high availability
- Integrating with other cloud services
- Building cloud-native applications
---
## When NOT to Use
Avoid google kubernetes engine when:
- Self-managed alternatives better suit your requirements
- Cost optimization strongly favors different solutions
- Specific vendor lock-in concerns exist
- Custom implementations provide better control
---
## Purpose and Use Cases
**Primary Purpose:** Provide cloud infrastructure for modern application deployments.
**Common Use Cases:**
1. **Production Deployments** — Running enterprise workloads
2. **Development Environments** — Quick provisioning for testing
3. **Disaster Recovery** — Backup and failover infrastructure
4. **Scaling Applications** — Auto-scaling for varying loads
5. **Data Processing** — Batch and stream processing jobs
---
## Architecture Design Patterns
### Pattern 1: High Availability Configuration
```bash
# Reference: Configure for high availability
# - Enable redundancy across availability zones
# - Set up automatic failover
# - Configure load balancing
# - Enable automated backups
```
### Pattern 2: Security Hardening
```bash
# Reference: Implement security best practices
# - Enable encryption at rest and in transit
# - Configure network security controls
# - Implement identity and access management
# - Enable audit logging
```
---
## Integration Approaches
### 1. Integration with Monitoring
Enable comprehensive observability:
- Real-time metrics collection
- Log aggregation and analysis
- Performance monitoring
- Alert configuration
### 2. Integration with Security
Implement defense-in-depth:
- Identity and access management
- Encryption at rest and in transit
- Network security controls
- Compliance monitoring
### 3. Integration with Other Services
Connect with platform services:
- Load balancing and traffic management
- Database services
- Storage solutions
- Container orchestration
---
## Common Pitfalls
### ❌ Pitfall 1: Missing Monitoring
**Problem:** No visibility into resource health and performance.
**Solution:**
- Enable comprehensive monitoring
- Set up log aggregation
- Configure alerting rules
- Review metrics regularly
### ❌ Pitfall 2: Inadequate Security
**Problem:** Resources exposed to unauthorized access.
**Solution:**
- Enforce identity and access management
- Enable encryption
- Configure network security groups
- Conduct security audits
### ❌ Pitfall 3: No Backup Strategy
**Problem:** Data loss with no recovery option.
**Solution:**
- Enable automated backups
- Test restore procedures
- Store backups in separate regions
- Document recovery processes
### ❌ Pitfall 4: Single Point of Failure
**Problem:** Service outage from single resource failure.
**Solution:**
- Enable high availability features
- Distribute across availability zones
- Implement automatic failover
- Design for graceful degradation
---
## Best Practices Summary
| Category | Best Practice |
|---|---|
| **Availability** | Enable high availability and auto-scaling |
| **Security** | Enforce IAM, encryption, and network controls |
| **Monitoring** | Enable comprehensive observability |
| **Backups** | Automated backups with regular testing |
| **Cost** | Right-size resources and monitor spending |
---
## Related Skills
| Skill | Purpose |
|---|---|
| `cncf-gcp-container-registry` | Related infrastructure service |
| `cncf-gcp-vpc` | Related infrastructure service |
| `cncf-gcp-iam` | Related infrastructure service |
| `cncf-gcp-cloud-monitoring` | Related infrastructure service |
---
## Core Workflow
1. **Assess Requirements** — Understand the use case, scale, integration needs, and existing infrastructure. **Checkpoint:** Document requirements, constraints, and success criteria.
2. **Design Architecture** — Plan component interactions, data flow, and deployment strategy using cloud-native best practices. **Checkpoint:** Verify the architecture addresses all requirements and follows CNCF conventions.
3. **Implement & Configure** — Create manifests, configurations, and deployment scripts. Include resource limits, health checks, and observability hooks. **Checkpoint:** Validate all YAML against schema and test in a staging environment.
4. **Deploy & Monitor** — Apply manifests to the cluster, verify component health, and confirm observability is working. **Checkpoint:** Confirm all pods/services are running, probes passing, and metrics/alerts configured.
---
## Constraints
### MUST DO
- Include at least one complete working YAML manifest example
- Note when content is auto-generated vs. manually verified
- Reference relevant CNCF project documentation
### MUST NOT DO
- Deploy manifests without testing in a staging environment first
- Use deprecated API versions (e.g., apps/v1beta1)
- Omit resource limits and requests in Kubernetes manifests
---
## Live References
> Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.
- [Primary Documentation](https://cloud.google.com/kubernetes-engine/docs/concepts/cluster-architecture)
- [API Reference or Getting Started](https://cloud.google.com/kubernetes-engine/docs/how-to/persistent-volumes/cloud-storage-fuse-overview)
- [Configuration Guide](https://cloud.google.com/kubernetes-engine/docs/how-to/workload-identity)
- [Best Practices](https://cloud.google.com/kubernetes-engine/docs/how-to/horizontal-pod-autoscaling)
- [Common Patterns or Tutorials](https://cloud.google.com/kubernetes-engine/docs/how-to/network-policy)