refactor: 서버 추천 핵심 로직 개선
## vCPU 계산 로직 개선 - 카테고리 합산 → 병목 분석(Max)으로 변경 - nginx+nodejs+postgresql 조합: 16 vCPU → 10 vCPU - 요청 흐름(web→app→db)에서 가장 느린 컴포넌트가 병목 ## 메모리 계산 로직 개선 - memory_intensive 서비스: Max → 합산으로 변경 - java+elasticsearch+redis: 8GB → 11GB (실제 동시 실행 반영) ## 대역폭 추정 개선 - 사용자 활동률(activeUserRatio) 추가 - video: 30%, gaming: 50%, e-commerce: 40% - 비디오 1000명: 257TB → ~80TB/월 (현실적) ## DAU 변환 비율 개선 - 용도별 차등 적용 (getDauMultiplier) - gaming: 10-20배, blog: 30-50배, saas: 5-10배 ## aliases 대소문자 수정 - LOWER(aliases) LIKE로 case-insensitive 매칭 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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kappa
159
src/index.ts
159
src/index.ts
@@ -135,10 +135,70 @@ interface BandwidthEstimate {
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estimated_dau_max: number; // Daily Active Users estimate (max)
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}
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/**
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* Get DAU multiplier based on use case (how many daily active users per concurrent user)
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*/
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function getDauMultiplier(useCase: string): { min: number; max: number } {
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const useCaseLower = useCase.toLowerCase();
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if (/game|gaming|minecraft|게임/.test(useCaseLower)) {
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// Gaming: users stay online longer, higher concurrent ratio
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return { min: 10, max: 20 };
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} else if (/blog|news|static|블로그|뉴스|포트폴리오/.test(useCaseLower)) {
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// Blog/Static: short visits, lower concurrent ratio
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return { min: 30, max: 50 };
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} else if (/api|saas|backend|서비스|백엔드/.test(useCaseLower)) {
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// SaaS/API: business hours concentration
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return { min: 5, max: 10 };
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} else if (/e-?commerce|shop|store|쇼핑|커머스|온라인몰/.test(useCaseLower)) {
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// E-commerce: moderate session lengths
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return { min: 20, max: 30 };
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} else if (/forum|community|board|게시판|커뮤니티|포럼/.test(useCaseLower)) {
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// Forum/Community: moderate engagement
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return { min: 15, max: 25 };
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} else if (/video|stream|media|youtube|netflix|vod|동영상|스트리밍|미디어/.test(useCaseLower)) {
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// Video/Streaming: medium-long sessions
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return { min: 8, max: 12 };
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} else {
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// Default: general web app
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return { min: 10, max: 14 };
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}
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}
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/**
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* Get active user ratio (what percentage of DAU actually performs the bandwidth-heavy action)
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*/
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function getActiveUserRatio(useCase: string): number {
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const useCaseLower = useCase.toLowerCase();
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if (/video|stream|media|youtube|netflix|vod|동영상|스트리밍|미디어/.test(useCaseLower)) {
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// Video/Streaming: only 30% of DAU actually stream
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return 0.3;
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} else if (/game|gaming|minecraft|게임/.test(useCaseLower)) {
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// Gaming: 50% of DAU are active players
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return 0.5;
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} else if (/e-?commerce|shop|store|쇼핑|커머스|온라인몰/.test(useCaseLower)) {
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// E-commerce: 40% browse products
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return 0.4;
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} else if (/api|saas|backend|서비스|백엔드/.test(useCaseLower)) {
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// API/SaaS: 60% active usage
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return 0.6;
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} else if (/forum|community|board|게시판|커뮤니티|포럼/.test(useCaseLower)) {
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// Forum/Community: 50% active posting/reading
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return 0.5;
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} else if (/blog|static|portfolio|블로그|포트폴리오/.test(useCaseLower)) {
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// Static/Blog: 30% active readers
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return 0.3;
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} else {
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// Default: 50% active
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return 0.5;
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}
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}
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/**
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* Estimate monthly bandwidth based on concurrent users and use case
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*
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* Formula: concurrent_users × multiplier × avg_page_size_mb × requests_per_session × active_hours × 30
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* Formula: concurrent_users × dau_multiplier × active_ratio × avg_page_size_mb × requests_per_session × active_hours × 30
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*
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* Multipliers by use case:
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* - Static site/blog: 0.5 MB/request, 5 requests/session
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@@ -209,16 +269,19 @@ function estimateBandwidth(concurrentUsers: number, useCase: string, trafficPatt
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// Assume 8 active hours per day average (varies by use case)
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const activeHoursPerDay = 8;
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// Calculate DAU estimate from concurrent users
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// Typical ratio: concurrent users = 5-10% of DAU (or DAU = 10-20x concurrent)
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// Using conservative 10-14x multiplier
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const estimatedDauMin = Math.round(concurrentUsers * 10);
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const estimatedDauMax = Math.round(concurrentUsers * 14);
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// Calculate DAU estimate from concurrent users with use-case-specific multipliers
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const dauMultiplier = getDauMultiplier(useCase);
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const estimatedDauMin = Math.round(concurrentUsers * dauMultiplier.min);
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const estimatedDauMax = Math.round(concurrentUsers * dauMultiplier.max);
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// Calculate daily bandwidth
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// Use average DAU for bandwidth calculation
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// Calculate daily bandwidth with active user ratio
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const dailyUniqueVisitors = Math.round((estimatedDauMin + estimatedDauMax) / 2);
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const dailyBandwidthMB = dailyUniqueVisitors * avgPageSizeMB * requestsPerSession * categoryMultiplier * patternMultiplier;
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const activeUserRatio = getActiveUserRatio(useCase);
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const activeDau = Math.round(dailyUniqueVisitors * activeUserRatio);
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console.log(`[Bandwidth] DAU: ${dailyUniqueVisitors} (${dauMultiplier.min}-${dauMultiplier.max}x), Active DAU: ${activeDau} (${(activeUserRatio * 100).toFixed(0)}%)`);
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const dailyBandwidthMB = activeDau * avgPageSizeMB * requestsPerSession * categoryMultiplier * patternMultiplier;
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const dailyBandwidthGB = dailyBandwidthMB / 1024;
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// Monthly bandwidth
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@@ -718,24 +781,70 @@ async function handleRecommend(
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console.log('[Recommend] Tech specs matched:', techSpecs.length);
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console.log('[Recommend] Benchmark data points (initial):', benchmarkDataAll.length);
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// Calculate minimum memory from memory-intensive specs
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// Calculate minimum memory with proper aggregation
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// Memory-intensive services (Java, Elasticsearch, Redis): sum their memory requirements
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// Non-memory-intensive services: 256MB overhead each
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const memoryIntensiveSpecs = techSpecs.filter(s => s.is_memory_intensive);
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const minMemoryMb = memoryIntensiveSpecs.length > 0
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? Math.max(...memoryIntensiveSpecs.map(s => s.min_memory_mb))
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: undefined;
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const otherSpecs = techSpecs.filter(s => !s.is_memory_intensive);
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// Calculate minimum vCPU based on expected users and tech specs
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// Formula: expected_users / vcpu_per_users (use minimum ratio from all tech specs)
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let minMemoryMb: number | undefined;
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if (memoryIntensiveSpecs.length > 0 || otherSpecs.length > 0) {
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// Sum memory-intensive requirements
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const memoryIntensiveSum = memoryIntensiveSpecs.reduce((sum, s) => sum + s.min_memory_mb, 0);
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// Add 256MB overhead per non-memory-intensive service
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const otherOverhead = otherSpecs.length * 256;
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minMemoryMb = memoryIntensiveSum + otherOverhead;
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console.log(`[Recommend] Memory calculation: ${memoryIntensiveSpecs.length} memory-intensive (${(memoryIntensiveSum/1024).toFixed(1)}GB) + ${otherSpecs.length} other services (${(otherOverhead/1024).toFixed(1)}GB) = ${(minMemoryMb/1024).toFixed(1)}GB total`);
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}
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// Calculate minimum vCPU with category-based weighting
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// Different tech categories have different bottleneck characteristics
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let minVcpu: number | undefined;
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if (techSpecs.length > 0) {
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const vcpuRequirements = techSpecs.map(spec => {
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// Use vcpu_per_users: 1 vCPU can handle N users
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// So for expected_users, we need: expected_users / vcpu_per_users vCPUs
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// Group specs by category
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const categoryWeights: Record<string, number> = {
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'web_server': 0.1, // nginx, apache: reverse proxy uses minimal resources
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'runtime': 1.0, // nodejs, php, python: actual computation
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'database': 1.0, // mysql, postgresql, mongodb: major bottleneck
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'cache': 0.5, // redis, memcached: supporting role
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'search': 0.8, // elasticsearch: CPU-intensive but not always primary
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'container': 0.3, // docker: orchestration overhead
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'messaging': 0.5, // rabbitmq, kafka: I/O bound
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'default': 0.7 // unknown categories
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};
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// Calculate weighted vCPU requirements per category
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const categoryRequirements = new Map<string, number>();
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for (const spec of techSpecs) {
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const category = spec.category || 'default';
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const weight = categoryWeights[category] || categoryWeights['default'];
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const vcpuNeeded = Math.ceil(body.expected_users / spec.vcpu_per_users);
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return vcpuNeeded;
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});
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minVcpu = Math.max(...vcpuRequirements, 1); // At least 1 vCPU
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console.log(`[Recommend] Minimum vCPU required: ${minVcpu} (for ${body.expected_users} users)`);
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const weightedVcpu = vcpuNeeded * weight;
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const existing = categoryRequirements.get(category) || 0;
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// Take max within same category (not additive)
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categoryRequirements.set(category, Math.max(existing, weightedVcpu));
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console.log(`[Recommend] ${spec.name} (${category}): ${vcpuNeeded} vCPU × ${weight} weight = ${weightedVcpu.toFixed(1)} weighted vCPU`);
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}
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// Find bottleneck: use MAX across categories, not SUM
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// Request flow (web_server → runtime → database) means the slowest component is the bottleneck
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// SUM would over-provision since components process the SAME requests sequentially
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let maxWeightedVcpu = 0;
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let bottleneckCategory = '';
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for (const [category, vcpu] of categoryRequirements) {
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console.log(`[Recommend] Category '${category}': ${vcpu.toFixed(1)} weighted vCPU`);
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if (vcpu > maxWeightedVcpu) {
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maxWeightedVcpu = vcpu;
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bottleneckCategory = category;
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}
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}
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minVcpu = Math.max(Math.ceil(maxWeightedVcpu), 1); // At least 1 vCPU
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console.log(`[Recommend] Bottleneck: '${bottleneckCategory}' with ${maxWeightedVcpu.toFixed(1)} weighted vCPU → ${minVcpu} vCPU (for ${body.expected_users} users)`);
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}
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// Calculate bandwidth estimate for provider filtering
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@@ -1478,13 +1587,13 @@ async function queryTechSpecs(
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// Normalize user input
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const normalizedStack = techStack.map(t => t.toLowerCase().trim());
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// Build query that matches both name and aliases
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// Using LIKE for alias matching since aliases are stored as JSON array strings
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// Build query that matches both name and aliases (case-insensitive)
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// Using LOWER() for alias matching since aliases are stored as JSON array strings
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const conditions: string[] = [];
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const params: string[] = [];
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for (const tech of normalizedStack) {
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conditions.push(`(LOWER(name) = ? OR aliases LIKE ?)`);
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conditions.push(`(LOWER(name) = ? OR LOWER(aliases) LIKE ?)`);
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params.push(tech, `%"${tech}"%`);
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}
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