2nd/10_Wiki/Topics/Coding/DevOps_Spinnaker_Tekton.md

---
id: devops-spinnaker-tekton
title: Spinnaker / Tekton — modern CI/CD pipelines
category: Coding
status: draft
source_trust_level: B
verification_status: conceptual
created_at: 2026-05-09
updated_at: 2026-05-09
tags: [devops, cicd, vibe-coding]
tech_stack: { language: "YAML", applicable_to: ["DevOps"] }
applied_in: []
aliases: [Spinnaker, Tekton, CI/CD pipeline, multi-cloud, continuous delivery, Netflix, K8s pipeline]
---

# Spinnaker / Tekton

> Modern CI/CD beyond Jenkins. **Spinnaker (multi-cloud), Tekton (K8s-native)**.

## 📖 핵심 개념
- Spinnaker: Netflix 의 multi-cloud CD.
- Tekton: K8s-native pipeline.
- 매 stage = container.
- GitOps 친화.

## 💻 코드 패턴

### Spinnaker pipeline (UI / JSON)
```json
{
  "name": "Deploy",
  "stages": [
    { "type": "bake", "package": "my-app", "baseOs": "ubuntu" },
    { "type": "deploy", "clusters": [{ "account": "aws", "region": "us-east-1" }] },
    { "type": "manualJudgment", "message": "Approve prod?" },
    { "type": "deploy", "clusters": [{ "account": "aws-prod" }] }
  ]
}
```

→ Multi-cloud (AWS, GCP, Azure, K8s).

### Spinnaker 의 강점
```
- Multi-cloud (이주 cluster).
- Canary (Kayenta automated analysis).
- Manual judgment (approval).
- Trigger (Jenkins, Docker, cron).
- Audit / RBAC.
```

### Tekton (K8s-native)
```yaml
apiVersion: tekton.dev/v1
kind: Pipeline
metadata:
  name: build-deploy
spec:
  tasks:
    - name: build
      taskRef: { name: build-image }
    - name: deploy
      runAfter: [build]
      taskRef: { name: deploy-app }
```

```yaml
apiVersion: tekton.dev/v1
kind: Task
metadata:
  name: build-image
spec:
  steps:
    - name: build
      image: gcr.io/kaniko-project/executor
      args: [--dockerfile=./Dockerfile, --destination=my-image]
```

→ 매 task = pod.

### PipelineRun
```yaml
apiVersion: tekton.dev/v1
kind: PipelineRun
metadata:
  name: pr-1
spec:
  pipelineRef: { name: build-deploy }
```

→ Trigger.

### Tekton Triggers
```yaml
apiVersion: triggers.tekton.dev/v1beta1
kind: EventListener
metadata:
  name: github-listener
spec:
  triggers:
    - name: github-push
      interceptors:
        - ref: { name: 'github' }
      bindings:
        - ref: { name: github-binding }
      template:
        ref: build-deploy-template
```

→ GitHub webhook 가 PipelineRun.

### vs Jenkins
```
Jenkins:
- Mature.
- Plugin 큰 ecosystem.
- Java VM.
- 큰 server.

Spinnaker:
- Multi-cloud first.
- Canary / blue-green.
- Netflix scale.

Tekton:
- K8s-native.
- 작은 (pod 별).
- GitOps.
```

→ K8s = Tekton.
Multi-cloud / 큰 enterprise = Spinnaker.
Legacy = Jenkins.

### vs GitHub Actions
```
GitHub Actions:
- GitHub 친화.
- Action marketplace.
- 작은-중간 project.

Tekton:
- K8s-native (자체 cluster).
- 큰 organization.

→ Modern startup = GHA.
큰 enterprise + K8s = Tekton.
```

### vs Argo Workflows
```
Argo Workflows:
- K8s-native (Tekton 와 비슷).
- DAG-based.
- 더 popular (Argo ecosystem).

Tekton:
- Linear pipeline.
- More 작은 + simpler.

→ Argo 가 더 powerful.
Tekton 가 더 simple.
```

### Spinnaker canary (Kayenta)
```json
{
  "stages": [
    {
      "type": "kayentaCanary",
      "canaryConfig": {
        "metricsAccountName": "datadog",
        "scoreThresholds": { "marginal": 75, "pass": 95 }
      }
    }
  ]
}
```

→ Datadog metrics 가 canary 의 OK / abort.

### Tekton + ArgoCD
```
1. PR merge → Tekton build image.
2. Tekton update Helm chart (git).
3. ArgoCD detect change → deploy.

→ Build (Tekton) + Deploy (ArgoCD).
```

### Spinnaker + ArgoCD
```
Spinnaker = orchestrator.
ArgoCD = K8s sync.

→ Spinnaker 가 multi-cluster orchestrate.
```

### Production
- **Netflix**: Spinnaker 의 creator.
- **Google**: 자체 (similar to Tekton).
- **Capital One**: Spinnaker.
- **IBM**: Tekton 의 contributor.

### When NOT?
```
Spinnaker:
- 작은 team (overkill).
- Single cloud (simpler tool).

Tekton:
- 작은 / non-K8s.
- GitHub-only (GHA 충분).
```

### Cost
```
Spinnaker: free (open) + 큰 운영 cost.
Tekton: free + K8s cost.
GitHub Actions: per-use.
Jenkins: free + 운영.

→ 운영 cost 가 큰 factor.
```

### Monitoring
```
- Pipeline duration.
- Failure rate.
- Queue depth.
- Stage-level metric.

→ Datadog / Prometheus.
```

### Best practice
```
1. Pipeline as code (git).
2. 매 stage 의 timeout.
3. Auto-rollback.
4. Manual approval (production).
5. Canary / blue-green.
6. Audit log.
```

## 🤔 의사결정 기준
| 상황 | 추천 |
|---|---|
| K8s-native | Tekton / Argo Workflows |
| Multi-cloud | Spinnaker |
| GitHub project | GitHub Actions |
| Legacy enterprise | Jenkins |
| 작은 startup | GHA / GitLab CI |
| Canary auto-analysis | Spinnaker (Kayenta) |

## ❌ 안티패턴
- **Spinnaker on single cloud**: overkill.
- **Tekton + non-K8s**: 잘못 선택.
- **모든 거 manual approval**: slow.
- **No timeout**: hang.
- **No audit**: incident response 어려움.

## 🤖 LLM 활용 힌트
- Spinnaker = multi-cloud + Netflix-scale.
- Tekton = K8s-native pipeline.
- Argo Workflows = Tekton 의 popular alternative.
- GitOps + Spinnaker / Tekton = production.

## 🔗 관련 문서
- [[DevOps_CI_CD_Pipeline_Patterns]]
- [[DevOps_ArgoCD_Apps_Deep]]
- [[DevOps_Argo_Rollouts]]