feat: 대역폭 추정 및 DAU 표시 기능 추가
- 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>
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DEPLOYMENT.md
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DEPLOYMENT.md
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# Deployment Checklist
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Complete checklist for deploying the Server Recommendation System to Cloudflare Workers.
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---
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## Pre-Deployment Checklist
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### Development Environment ✅
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- [x] Node.js 18+ installed
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- [x] Dependencies installed (`npm install`)
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- [x] TypeScript compilation passes (`npm run typecheck`)
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- [x] Code structure verified
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### Cloudflare Account
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- [ ] Cloudflare account created/logged in
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- [ ] Wrangler CLI authenticated (`npx wrangler login`)
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---
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## Step-by-Step Deployment
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### Step 1: Create D1 Database (2 minutes)
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```bash
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# Create database
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npx wrangler d1 create server-recommend-db
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```
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**Expected Output:**
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```
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✅ Successfully created DB 'server-recommend-db'!
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[[d1_databases]]
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binding = "DB"
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database_name = "server-recommend-db"
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database_id = "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
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```
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**Action Required:**
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- [ ] Copy the `database_id` value
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- [ ] Open `wrangler.toml`
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- [ ] Replace empty `database_id = ""` with your actual ID
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- [ ] Save file
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---
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### Step 2: Create KV Namespace (1 minute)
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```bash
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# Create KV namespace
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npx wrangler kv:namespace create CACHE
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```
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**Expected Output:**
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```
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🌀 Creating namespace with title "server-recommend-CACHE"
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✨ Success!
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Add the following to your configuration file:
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[[kv_namespaces]]
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binding = "CACHE"
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id = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
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```
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**Action Required:**
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- [ ] Copy the `id` value
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- [ ] Open `wrangler.toml`
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- [ ] Replace empty `id = ""` with your actual ID
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- [ ] Save file
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**Verify wrangler.toml:**
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```toml
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[[d1_databases]]
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binding = "DB"
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database_name = "server-recommend-db"
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database_id = "YOUR_ACTUAL_DATABASE_ID" # ← Should be filled
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[[kv_namespaces]]
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binding = "CACHE"
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id = "YOUR_ACTUAL_KV_ID" # ← Should be filled
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```
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---
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### Step 3: Initialize Database (2 minutes)
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```bash
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# Create tables and indexes
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npx wrangler d1 execute server-recommend-db --file=schema.sql
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```
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**Expected Output:**
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```
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🌀 Mapping SQL input into an array of statements
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🌀 Executing on server-recommend-db (xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx):
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🚣 Executed 7 commands in X.XXXs
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```
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**Checklist:**
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- [ ] Command executed successfully
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- [ ] No errors in output
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- [ ] Tables created (providers, servers, server_regions)
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- [ ] Indexes created
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**Verify:**
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```bash
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# Check tables
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npx wrangler d1 execute server-recommend-db --command "SELECT name FROM sqlite_master WHERE type='table'"
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```
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---
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### Step 4: Seed Sample Data (1 minute)
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```bash
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# Insert sample server data
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npx wrangler d1 execute server-recommend-db --file=seed.sql
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```
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**Expected Output:**
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```
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🌀 Mapping SQL input into an array of statements
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🌀 Executing on server-recommend-db (xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx):
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🚣 Executed XX commands in X.XXXs
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```
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**Verify:**
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```bash
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# Check row counts
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npx wrangler d1 execute server-recommend-db --command "SELECT COUNT(*) as count FROM servers"
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```
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**Expected:** `count = 13` (sample data includes 13 servers)
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**Checklist:**
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- [ ] Command executed successfully
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- [ ] 4 providers inserted
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- [ ] 13 servers inserted
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- [ ] Regional data inserted
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---
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### Step 5: Local Testing (2 minutes)
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```bash
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# Start development server
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npm run dev
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```
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**Expected Output:**
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```
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⛅️ wrangler 4.60.0
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-------------------
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⎔ Starting local server...
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[wrangler:inf] Ready on http://localhost:8787
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```
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**In another terminal:**
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```bash
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# Run test suite
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./test.sh
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```
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**Or manual tests:**
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```bash
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# Health check
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curl http://localhost:8787/api/health
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# List servers
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curl http://localhost:8787/api/servers | jq .
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# Get recommendation
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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": 80
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}' | jq .
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```
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**Checklist:**
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- [ ] Health endpoint returns 200
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- [ ] Servers endpoint returns data
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- [ ] Recommend endpoint works
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- [ ] AI generates recommendations
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- [ ] Cache works (second request faster)
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- [ ] No errors in console
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---
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### Step 6: Deploy to Production (1 minute)
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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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**Expected Output:**
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```
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⛅️ wrangler 4.60.0
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-------------------
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Total Upload: XX.XX KiB / gzip: XX.XX KiB
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Uploaded server-recommend (X.XX sec)
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Published server-recommend (X.XX sec)
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https://server-recommend.YOUR_SUBDOMAIN.workers.dev
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Current Deployment ID: xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx
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```
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**Action Required:**
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- [ ] Copy the deployed worker URL
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- [ ] Save it for testing
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**Checklist:**
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- [ ] Deployment successful
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- [ ] Worker URL obtained
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- [ ] No deployment errors
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---
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### Step 7: Production Testing (2 minutes)
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**Replace with your actual worker URL:**
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```bash
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export WORKER_URL="https://server-recommend.YOUR_SUBDOMAIN.workers.dev"
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```
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**Run tests:**
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```bash
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# Test with your URL
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./test.sh $WORKER_URL
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# Or manual tests
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curl $WORKER_URL/api/health
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curl "$WORKER_URL/api/servers?minCpu=4"
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curl -X POST $WORKER_URL/api/recommend \
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-H "Content-Type: application/json" \
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-d @test-request.json | jq .
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```
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**Checklist:**
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- [ ] Health check works
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- [ ] Server listing works
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- [ ] Recommendations work
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- [ ] Response times acceptable
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- [ ] No 500 errors
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---
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### Step 8: Monitor Deployment (Ongoing)
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```bash
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# View real-time logs
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npx wrangler tail
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```
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**Cloudflare Dashboard:**
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1. Go to https://dash.cloudflare.com/
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2. Navigate to Workers & Pages
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3. Click on `server-recommend`
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4. View metrics:
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- [ ] Request rate
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- [ ] Success rate
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- [ ] Errors
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- [ ] Invocation duration
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---
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## Post-Deployment Verification
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### Functional Tests
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- [ ] ✅ Health endpoint: `GET /api/health`
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- [ ] ✅ Server list: `GET /api/servers`
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- [ ] ✅ Server filtering: `GET /api/servers?minCpu=4&minMemory=8`
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- [ ] ✅ Recommendations: `POST /api/recommend`
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- [ ] ✅ Cache working (second request faster)
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- [ ] ✅ CORS headers present
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- [ ] ✅ Error handling works
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### Performance Tests
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- [ ] Cold start: < 100ms
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- [ ] Cached response: < 100ms
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- [ ] AI response: 2-5s (first request)
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- [ ] Database query: < 50ms
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### Security Tests
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- [ ] Input validation working
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- [ ] SQL injection protected
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- [ ] Error messages safe
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- [ ] CORS properly configured
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---
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## Troubleshooting
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### Issue: "Database not found"
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**Solution:**
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1. Verify `database_id` in `wrangler.toml`
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2. List databases: `npx wrangler d1 list`
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3. Re-create if needed: `npx wrangler d1 create server-recommend-db`
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### Issue: "KV namespace not found"
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**Solution:**
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1. Verify `id` in `wrangler.toml`
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2. List namespaces: `npx wrangler kv:namespace list`
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3. Re-create if needed: `npx wrangler kv:namespace create CACHE`
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### Issue: "No servers found"
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**Solution:**
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1. Verify seed data: `npx wrangler d1 execute server-recommend-db --command "SELECT COUNT(*) FROM servers"`
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2. Re-seed if needed: `npx wrangler d1 execute server-recommend-db --file=seed.sql`
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### Issue: "Workers AI error"
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**Solution:**
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1. Workers AI is automatic, no configuration needed
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2. Check quota in Cloudflare dashboard
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3. Verify model name: `@cf/meta/llama-3.1-8b-instruct`
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### Issue: "Deployment fails"
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**Solution:**
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1. Check `wrangler.toml` syntax
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2. Verify all IDs filled
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3. Run `npm run typecheck`
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4. Check authentication: `npx wrangler whoami`
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---
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## Rollback Procedure
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If deployment has issues:
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```bash
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# Rollback to previous deployment
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npx wrangler rollback
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# Or deploy specific version
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npx wrangler rollback --deployment-id <DEPLOYMENT_ID>
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```
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---
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## Success Criteria
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Deployment is successful when:
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✅ All 3 API endpoints return 200 status
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✅ AI recommendations generated correctly
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✅ Cache hit rate > 0% after multiple requests
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✅ No 500 errors in logs
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✅ Response times within targets
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✅ Database queries working
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✅ Sample data accessible
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---
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## Next Steps After Deployment
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### Immediate
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1. [ ] Test all endpoints thoroughly
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2. [ ] Monitor logs for errors
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3. [ ] Verify cache working
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4. [ ] Document worker URL
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### Short Term (1 week)
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1. [ ] Add production server data
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2. [ ] Monitor usage and performance
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3. [ ] Set up alerts for errors
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4. [ ] Adjust cache TTL if needed
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### Long Term (1 month)
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1. [ ] Implement rate limiting
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2. [ ] Add authentication
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3. [ ] Set up custom domain
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4. [ ] Enhance AI prompts
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5. [ ] Add analytics
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6. [ ] Create admin interface
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---
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## Resource Limits Checklist
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### Free Tier (Daily Limits)
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- [ ] Workers: < 100,000 requests/day
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- [ ] Workers AI: < 10,000 neurons/day
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- [ ] D1: < 5M reads/day, < 100K writes/day
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- [ ] KV: < 100,000 reads/day, < 1,000 writes/day
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### Monitoring Alerts
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Set up alerts for:
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- [ ] Request rate > 80% of limit
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- [ ] Error rate > 1%
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- [ ] Response time > 5s (95th percentile)
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- [ ] AI quota > 80% usage
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---
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## Support Resources
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- **Cloudflare Workers Docs:** https://developers.cloudflare.com/workers/
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- **Workers AI Docs:** https://developers.cloudflare.com/workers-ai/
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- **D1 Docs:** https://developers.cloudflare.com/d1/
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- **Community Discord:** https://discord.gg/cloudflaredev
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- **Status Page:** https://www.cloudflarestatus.com/
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---
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## Deployment Complete! 🎉
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When all checkboxes are marked:
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- ✅ Worker deployed successfully
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- ✅ All endpoints tested
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- ✅ Monitoring in place
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- ✅ No errors detected
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**Your Server Recommendation System is now LIVE!**
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Worker URL: `https://server-recommend.YOUR_SUBDOMAIN.workers.dev`
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Share this URL with users to start receiving recommendations!
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Reference in New Issue
Block a user