4cb9da06dc
- 동시접속자 기반 월간 대역폭 자동 추정 - DAU(일일활성사용자) 추정치 표시 (동접 × 10-14) - 대역폭 기반 Linode/Vultr 자동 선택 로직 - 비용 분석에 대역폭 비용 포함 - 지역 미선택시 서울/도쿄/오사카/싱가포르 기본 표시 - 지역별 서버 분리 표시 (GROUP BY instance + region) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
133 lines
2.6 KiB
Markdown
133 lines
2.6 KiB
Markdown
# Server Recommendation System - Setup Guide
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## 1. Create Cloudflare Resources
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### Create D1 Database
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```bash
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# Create D1 database
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npx wrangler d1 create server-recommend-db
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# Copy the database_id from output and update wrangler.toml
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```
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### Create KV Namespace
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```bash
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# Create KV namespace for caching
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npx wrangler kv:namespace create CACHE
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# Copy the id from output and update wrangler.toml
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```
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## 2. Initialize Database Schema
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```bash
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# Execute schema.sql against your D1 database
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npx wrangler d1 execute server-recommend-db --file=schema.sql
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```
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## 3. Seed Sample Data (Optional)
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Create a file `seed.sql` with sample server data and run:
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```bash
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npx wrangler d1 execute server-recommend-db --file=seed.sql
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```
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## 4. Development
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```bash
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# Start local development server
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npm run dev
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# Type checking
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npm run typecheck
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```
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## 5. Deployment
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```bash
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# Deploy to Cloudflare Workers
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npm run deploy
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```
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## API Endpoints
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### Health Check
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```bash
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curl https://your-worker.workers.dev/api/health
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```
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### Get Servers
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```bash
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curl "https://your-worker.workers.dev/api/servers?minCpu=4&minMemory=8"
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```
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### Request Recommendations
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```bash
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curl -X POST https://your-worker.workers.dev/api/recommend \
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-H "Content-Type: application/json" \
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-d '{
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"cpu_cores": 4,
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"memory_gb": 8,
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"storage_gb": 100,
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"network_bandwidth_mbps": 1000,
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"sla_requirement": 99.9,
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"budget_monthly": 200,
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"regions": ["us-east-1", "us-west-2"]
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}'
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```
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## Environment Variables
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No environment variables needed - all bindings are configured in `wrangler.toml`:
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- `AI`: Workers AI binding (automatic)
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- `DB`: D1 Database binding
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- `CACHE`: KV namespace binding
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## Testing Workers AI Integration
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The worker uses `@cf/meta/llama-3.1-8b-instruct` model. Test with:
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```bash
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# Local development automatically uses Workers AI
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npm run dev
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# Make test request
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curl -X POST http://localhost:8787/api/recommend \
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-H "Content-Type: application/json" \
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-d '{
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"cpu_cores": 2,
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"memory_gb": 4,
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"storage_gb": 50
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}'
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```
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## Cache Strategy
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Recommendations are cached in KV for 1 hour based on request parameters:
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- Cache key format: `recommend:cpu:X|mem:Y|stor:Z|...`
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- TTL: 3600 seconds (1 hour)
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- Cache hit/miss logged in console
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## Error Handling
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All endpoints return proper HTTP status codes:
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- `200`: Success
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- `400`: Invalid request (validation error)
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- `404`: Endpoint not found
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- `500`: Internal server error
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CORS is enabled for all origins (`Access-Control-Allow-Origin: *`).
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## Monitoring
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Enable observability in `wrangler.toml`:
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```toml
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[observability]
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enabled = true
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```
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View logs:
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```bash
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npx wrangler tail
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```
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