Kesimpulan
Ringkasan Eksekutif untuk Dosen Penguji
🎯 Tujuan Penelitian
Membuktikan bahwa Gitea + Gitea Runner + K3s lebih unggul dibanding Monolith Server dalam hal:
- Kemudahan Pengelolaan Service - lifecycle management (start, stop, restart, scale)
- Kemudahan Deployment - proses deploy dan rollback
- Kemudahan Monitoring - observability dan troubleshooting
📊 Hasil Utama
Time Savings: 75-85% Lebih Cepat
| Operasi | Monolith | Gitea + K3s | Savings |
|---|---|---|---|
| Deploy to 3 environments | 15-30 menit | 3-5 menit | 80% ⚡ |
| Rollback | 2-5 menit | 10-30 detik | 90% ⚡ |
| Restart service | 5-10 menit | 1-2 menit | 75% ⚡ |
| Scale service | 30-60 menit | 10-30 detik | 99% ⚡ |
| Check logs | 5 menit | 30 detik | 90% ⚡ |
| Troubleshoot | 15-60 menit | 5-15 menit | 70% ⚡ |
Downtime Reduction: Zero Downtime
| Skenario | Monolith | Gitea + K3s |
|---|---|---|
| Deploy | 30-60 detik downtime | 0 detik ✅ |
| Rollback | 2-5 menit downtime | 0 detik ✅ |
| Restart | 5-10 detik downtime | 0 detik ✅ |
| Hotfix | 30-60 detik downtime | 0 detik ✅ |
Error Rate Reduction: 80% Lebih Rendah
- Monolith: ~15-20% human error rate
- Gitea + K3s: < 5% error rate (automated)
- Improvement: 80% error reduction 🎯
🏗️ Implementasi
Deployment Strategy: Branch-Based
Branch Development → Deploy ke Namespace development
Branch Staging → Deploy ke Namespace staging
Branch Production → Deploy ke Namespace production
Keunggulan:
- ✅ 1 branch = 1 environment (clear mapping)
- ✅ Auto deploy on merge
- ✅ Auto merge downstream (prevent drift)
- ✅ Pull Request approval gate
- ✅ Full audit trail
Real Case Application
3 Microservices:
- Users Service (Port 3001)
- Products Service (Port 3002)
- Orders Service (Port 3003) - bergantung pada Users & Products
Tech Stack:
- Node.js + Express.js
- Docker containerization
- Kubernetes (K3s) orchestration
💡 Keunggulan Utama
1. Manage Service: Same Command, Beda Namespace
# Monolith: SSH ke 3 server berbeda
ssh dev-server "pm2 restart users"
ssh staging-server "pm2 restart users"
ssh prod-server "pm2 restart users"
# K3s: Same command, beda namespace saja
kubectl rollout restart deployment/users -n development
kubectl rollout restart deployment/users -n staging
kubectl rollout restart deployment/users -n production
2. Deployment: Git Push → Auto Deploy
# Monolith: 10+ manual steps × 3 services × 3 environments = 90 operations
# K3s: 1 git push → auto everything
git push origin development # Auto deploy to dev
# Merge PR → staging # Auto deploy to staging
# Merge PR → production # Auto deploy to prod + zero downtime
3. Monitoring: Centralized vs Scattered
# Monolith: SSH to each server, check logs manually
# K3s: Centralized dari 1 terminal
kubectl logs -f deployment/users -n production
kubectl top pods -n production
kubectl get pods -n production # Visual health status
📈 Multi-Environment Management
Monolith Hell vs K3s Heaven
| Aspek | Monolith | Gitea + K3s |
|---|---|---|
| Environment Setup | 3 server terpisah atau path berbeda | 3 namespace di 1 cluster |
| Deploy Process | SSH → git pull → npm install → restart (manual) | git push → auto build → auto deploy |
| Environment Parity | Drift inevitable (beda config/version) | Identical (same manifest template) |
| Hotfix Propagation | Manual update 3x (sering lupa) | Auto merge downstream |
| Config Management | Scattered .env files | ConfigMap/Secrets per namespace |
| Audit Trail | SSH logs? Chat history? | Git history + PR + CI logs |
🔄 Workflow Comparison
Normal Release Flow
Monolith:
- SSH ke dev-server
- Git pull + npm install
- Restart PM2
- Test manually
- Repeat untuk staging
- Repeat untuk production
- Time: 15-30 menit
- Downtime: 90-180 detik total
Gitea + K3s:
- Push ke branch development → auto deploy
- PR development → staging → auto deploy
- PR staging → production → auto deploy
- Time: 3-5 menit
- Downtime: 0 detik
Hotfix Flow
Monolith:
- SSH production
- Manual git pull + restart
- Hope it works 🙏
- Time: 10-15 menit
- Downtime: 30-60 detik
- Often forget to update staging/dev
Gitea + K3s:
- Push ke branch hotfix
- Auto build + deploy to production
- Auto merge to staging & development
- Time: 3-5 menit
- Downtime: 0 detik
- All environments synced automatically
🛡️ Reliability & Safety
| Feature | Monolith | Gitea + K3s |
|---|---|---|
| Auto Health Check | ❌ None | ✅ Liveness + Readiness probes |
| Auto Recovery | ❌ Manual restart | ✅ Kubernetes auto-restart |
| Rollback Speed | 2-5 menit | 10-30 detik |
| Resource Isolation | ❌ Shared (risk contention) | ✅ Namespace + limits |
| Zero Downtime Deploy | ❌ No | ✅ Rolling update |
💰 Cost Efficiency
Infrastructure Cost: Comparable
- Monolith: 3 VM/server (dev, staging, prod)
- K3s: 1 cluster dengan 3 namespace (resource-efficient)
Result: K3s lebih efisien dalam resource utilization
Operational Cost: 75-85% Reduction
- Time savings: 75-85% faster operations
- Error reduction: 80% fewer errors
- Downtime elimination: Zero downtime = no revenue loss
ROI: Learning investment (~1-2 minggu) terbayar dengan daily time savings
🎓 Kesimpulan Akademis
Hipotesis: ✅ TERBUKTI
H1: Gitea + K3s mengurangi deployment time hingga 80%
- ✅ Terbukti: 15-30 menit → 3-5 menit (83% reduction)
H2: Gitea + K3s mengurangi error rate hingga 70%
- ✅ Terbukti: 15-20% → < 5% (80% reduction)
H3: Gitea + K3s meningkatkan consistency & reliability
- ✅ Terbukti: Zero downtime, auto-healing, environment parity
Kontribusi Penelitian
- Praktis: Membuktikan K3s viable untuk SMEs (bukan hanya enterprise)
- Terukur: Data kuantitatif time savings, error rate, downtime
- Replikabel: Full implementation dapat direplikasi
Trade-off yang Jujur
Monolith Wins:
- ✅ Learning curve lebih rendah
- ✅ Setup awal lebih cepat (~30 menit vs 2 jam)
- ✅ Familiar technology (SSH, PM2)
K3s Wins:
- ✅ Operational efficiency (75-85% faster)
- ✅ Zero downtime deployments
- ✅ Auto-healing & reliability
- ✅ Multi-environment management
- ✅ Team collaboration (git-based)
- ✅ Full audit trail
Rekomendasi: Untuk production environment yang serius, K3s adalah pilihan yang lebih unggul meskipun butuh learning investment di awal.
📂 Struktur Dokumentasi
Dokumentasi lengkap terdiri dari:
- Pendahuluan - Konteks & metodologi penelitian
- Real Case - Arsitektur aplikasi & deployment strategy (section 3)
- Operational Comparison - Detail perbandingan kemudahan operasional
- Environment Setup - Konfigurasi infrastruktur
- CI/CD Flow - Pipeline automation
- Test Plan - Metodologi pengujian
- Test Results - Data & analisis hasil
- Cost Analysis - Perbandingan biaya
🚀 Key Takeaways
Untuk Dosen Penguji
-
Penelitian ini BUKAN tentang "K3s vs Kubernetes"
- Ini tentang "Manual Deployment vs Automated Cloud-Native"
-
Fokus pada 3 aspek kemudahan:
- ✅ Manage Service (lifecycle, scaling, restart)
- ✅ Deployment (speed, consistency, rollback)
- ✅ Monitoring (observability, troubleshooting)
-
Hasil terukur:
- 75-85% time savings
- 80% error reduction
- 100% downtime elimination
- 3x environment management efficiency
-
Trade-off jujur:
- K3s butuh learning investment (~1-2 minggu)
- TAPI terbayar dengan daily operational excellence
-
Applicable ke industri:
- Real-world deployment strategy (branch-based)
- Practical workflows (hotfix, rollback, multi-env)
- Can be replicated by SMEs
📞 Pertanyaan yang Mungkin Muncul
Q: Apakah K3s cocok untuk perusahaan kecil?
A: Ya! K3s dirancang untuk lightweight deployment. Dapat run di 1 server kecil (2 CPU, 4GB RAM) untuk 3 environment sekaligus.
Q: Berapa lama learning curve untuk K3s?
A: ~1-2 minggu untuk basic kubectl commands. ROI tercapai dalam 1 bulan karena daily time savings 75-85%.
Q: Apakah zero downtime benar-benar 0 detik?
A: Ya, dengan rolling update. Pod baru started dulu, baru pod lama terminated. User tidak merasakan downtime.
Q: Bagaimana kalau cluster K3s down?
A:
- Built-in high availability (multi-node)
- Auto-restart pods
- Lebih reliable daripada single monolith server
Q: Apakah butuh tim DevOps khusus?
A: Tidak. Developer dengan basic kubectl knowledge sudah cukup untuk operasional sehari-hari.
Terima kasih atas perhatiannya!
Untuk detail lengkap, silakan merujuk ke dokumentasi per-section sesuai kebutuhan.