--- name: replit-load-scale description: | Implement Replit load testing, auto-scaling, and capacity planning strategies. Use when running performance tests, configuring horizontal scaling, or planning capacity for Replit integrations. Trigger with phrases like "replit load test", "replit scale", "replit performance test", "replit capacity", "replit k6", "replit benchmark". allowed-tools: Read, Write, Edit, Bash(k6:*), Bash(kubectl:*) version: 1.0.0 license: MIT author: Jeremy Longshore --- # Replit Load & Scale ## Overview Load testing, scaling strategies, and capacity planning for Replit integrations. ## Prerequisites - k6 load testing tool installed - Kubernetes cluster with HPA configured - Prometheus for metrics collection - Test environment API keys ## Load Testing with k6 ### Basic Load Test ```javascript // replit-load-test.js import http from 'k6/http'; import { check, sleep } from 'k6'; export const options = { stages: [ { duration: '2m', target: 10 }, // Ramp up { duration: '5m', target: 10 }, // Steady state { duration: '2m', target: 50 }, // Ramp to peak { duration: '5m', target: 50 }, // Stress test { duration: '2m', target: 0 }, // Ramp down ], thresholds: { http_req_duration: ['p(95)<500'], http_req_failed: ['rate<0.01'], }, }; export default function () { const response = http.post( 'https://api.replit.com/v1/resource', JSON.stringify({ test: true }), { headers: { 'Content-Type': 'application/json', 'Authorization': `Bearer ${__ENV.REPLIT_API_KEY}`, }, } ); check(response, { 'status is 200': (r) => r.status === 200, 'latency < 500ms': (r) => r.timings.duration < 500, }); sleep(1); } ``` ### Run Load Test ```bash # Install k6 brew install k6 # macOS # or: sudo apt install k6 # Linux # Run test k6 run --env REPLIT_API_KEY=${REPLIT_API_KEY} replit-load-test.js # Run with output to InfluxDB k6 run --out influxdb=http://localhost:8086/k6 replit-load-test.js ``` ## Scaling Patterns ### Horizontal Scaling ```yaml # kubernetes HPA apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: replit-integration-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: replit-integration minReplicas: 2 maxReplicas: 20 metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 70 - type: Pods pods: metric: name: replit_queue_depth target: type: AverageValue averageValue: 100 ``` ### Connection Pooling ```typescript import { Pool } from 'generic-pool'; const replitPool = Pool.create({ create: async () => { return new ReplitClient({ apiKey: process.env.REPLIT_API_KEY!, }); }, destroy: async (client) => { await client.close(); }, max: 20, min: 5, idleTimeoutMillis: 30000, }); async function withReplitClient( fn: (client: ReplitClient) => Promise ): Promise { const client = await replitPool.acquire(); try { return await fn(client); } finally { replitPool.release(client); } } ``` ## Capacity Planning ### Metrics to Monitor | Metric | Warning | Critical | |--------|---------|----------| | CPU Utilization | > 70% | > 85% | | Memory Usage | > 75% | > 90% | | Request Queue Depth | > 100 | > 500 | | Error Rate | > 1% | > 5% | | P95 Latency | > 1000ms | > 3000ms | ### Capacity Calculation ```typescript interface CapacityEstimate { currentRPS: number; maxRPS: number; headroom: number; scaleRecommendation: string; } function estimateReplitCapacity( metrics: SystemMetrics ): CapacityEstimate { const currentRPS = metrics.requestsPerSecond; const avgLatency = metrics.p50Latency; const cpuUtilization = metrics.cpuPercent; // Estimate max RPS based on current performance const maxRPS = currentRPS / (cpuUtilization / 100) * 0.7; // 70% target const headroom = ((maxRPS - currentRPS) / currentRPS) * 100; return { currentRPS, maxRPS: Math.floor(maxRPS), headroom: Math.round(headroom), scaleRecommendation: headroom < 30 ? 'Scale up soon' : headroom < 50 ? 'Monitor closely' : 'Adequate capacity', }; } ``` ## Benchmark Results Template ```markdown ## Replit Performance Benchmark **Date:** YYYY-MM-DD **Environment:** [staging/production] **SDK Version:** X.Y.Z ### Test Configuration - Duration: 10 minutes - Ramp: 10 → 100 → 10 VUs - Target endpoint: /v1/resource ### Results | Metric | Value | |--------|-------| | Total Requests | 50,000 | | Success Rate | 99.9% | | P50 Latency | 120ms | | P95 Latency | 350ms | | P99 Latency | 800ms | | Max RPS Achieved | 150 | ### Observations - [Key finding 1] - [Key finding 2] ### Recommendations - [Scaling recommendation] ``` ## Instructions ### Step 1: Create Load Test Script Write k6 test script with appropriate thresholds. ### Step 2: Configure Auto-Scaling Set up HPA with CPU and custom metrics. ### Step 3: Run Load Test Execute test and collect metrics. ### Step 4: Analyze and Document Record results in benchmark template. ## Output - Load test script created - HPA configured - Benchmark results documented - Capacity recommendations defined ## Error Handling | Issue | Cause | Solution | |-------|-------|----------| | k6 timeout | Rate limited | Reduce RPS | | HPA not scaling | Wrong metrics | Verify metric name | | Connection refused | Pool exhausted | Increase pool size | | Inconsistent results | Warm-up needed | Add ramp-up phase | ## Examples ### Quick k6 Test ```bash k6 run --vus 10 --duration 30s replit-load-test.js ``` ### Check Current Capacity ```typescript const metrics = await getSystemMetrics(); const capacity = estimateReplitCapacity(metrics); console.log('Headroom:', capacity.headroom + '%'); console.log('Recommendation:', capacity.scaleRecommendation); ``` ### Scale HPA Manually ```bash kubectl scale deployment replit-integration --replicas=5 kubectl get hpa replit-integration-hpa ``` ## Resources - [k6 Documentation](https://k6.io/docs/) - [Kubernetes HPA](https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/) - [Replit Rate Limits](https://docs.replit.com/rate-limits) ## Next Steps For reliability patterns, see `replit-reliability-patterns`.

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