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Load Testing Framework

Comprehensive load testing system for performance analysis and optimization.

Features

1. Realistic Load Testing Scenarios

Create and manage load testing scenarios with configurable parameters.

import { LoadTestScenario } from './loadTesting/index.js';

const scenario = new LoadTestScenario('payment-flow', 'Payment processing test');
scenario.addRequest('GET', '/api/health');
scenario.addRequest('POST', '/api/stellar/account/create', {}, 1);
scenario.addRequest('POST', '/api/stellar/payment/send', { amount: '100' }, 5);
scenario.setDuration(120).setRampUp(20).setConcurrency(50);
await scenario.save();

API Endpoint:

  • POST /api/load-testing/scenarios/create - Create scenario

2. Performance Baseline Establishment

Establish and track performance baselines for regression detection.

import { PerformanceBaseline, LoadTestRunner } from './loadTesting/index.js';

const runner = new LoadTestRunner();
const results = await runner.runScenario(scenario);
const baseline = new PerformanceBaseline('payment-flow');
baseline.calculateFromResults(results);
await baseline.save();

API Endpoints:

  • POST /api/load-testing/baseline/establish - Establish baseline
  • GET /api/load-testing/baseline/latest/:scenarioName - Get latest baseline

3. Automated Performance Regression Testing

Detect performance regressions automatically.

import { regressionTester } from './loadTesting/index.js';

const regressions = regressionTester.detectRegression(currentResults, baseline);
const report = regressionTester.generateReport(regressions);
// { status: 'PASS' | 'WARN' | 'FAIL', summary: {...}, regressions: [...] }

API Endpoint:

  • POST /api/load-testing/regression/check - Check for regressions

4. Capacity Planning Tools

Calculate maximum capacity and scaling needs.

import { capacityPlanner } from './loadTesting/index.js';

// Calculate current capacity
const capacity = capacityPlanner.calculateCapacity(results, targetErrorRate);
// { currentThroughput, maxSafeThroughput, maxCapacity, headroom, recommendations }

// Project scaling needs
const projection = capacityPlanner.estimateScalingNeeds(throughput, growthRate, months);
// { currentThroughput, projectedThroughput, scalingFactor, recommendations }

API Endpoints:

  • POST /api/load-testing/capacity/calculate - Calculate capacity
  • POST /api/load-testing/capacity/project - Project scaling needs

5. Performance Bottleneck Identification

Identify and analyze performance bottlenecks.

import { bottleneckAnalyzer } from './loadTesting/index.js';

const bottlenecks = bottleneckAnalyzer.analyze(results);
const recommendations = bottleneckAnalyzer.getRecommendations(bottlenecks);

API Endpoint:

  • POST /api/load-testing/bottlenecks/analyze - Analyze bottlenecks

6. Performance Alerting

Real-time performance alerting with configurable thresholds.

import { performanceAlerting } from './loadTesting/index.js';

performanceAlerting.setThreshold('avgResponseTime', 1000);
performanceAlerting.setThreshold('errorRate', 5);

const alerts = performanceAlerting.checkMetrics(results);
await performanceAlerting.saveAlerts();

API Endpoints:

  • POST /api/load-testing/alerts/check - Check metrics and generate alerts
  • GET /api/load-testing/alerts - Get all alerts
  • GET /api/load-testing/alerts/critical - Get critical alerts

7. Optimization Recommendations

Get actionable optimization recommendations.

import { optimizationRecommender } from './loadTesting/index.js';

const recommendations = optimizationRecommender.generateRecommendations(results, bottlenecks);
const prioritized = optimizationRecommender.prioritizeRecommendations(recommendations);

API Endpoint:

  • POST /api/load-testing/recommendations - Get optimization recommendations

8. Load Testing Execution

Run load tests and collect results.

import { LoadTestRunner } from './loadTesting/index.js';

const runner = new LoadTestRunner();
const results = await runner.runScenario(scenario, 'http://localhost:3001');
const saved = await runner.saveResults('payment-flow');

API Endpoints:

  • POST /api/load-testing/run - Run load test
  • GET /api/load-testing/results/:scenarioName - Get test results

Metrics Collected

  • Response Time: Min, max, average, p50, p95, p99
  • Throughput: Requests per second
  • Error Rate: Percentage of failed requests
  • Success Count: Total successful requests
  • Error Count: Total failed requests
  • Duration: Total test duration

Storage Structure

data/load-tests/
├── scenarios/          # Test scenarios (JSON)
├── baselines/          # Performance baselines (JSON)
├── results/            # Test results (JSON)
└── alerts/             # Performance alerts (JSON)

Performance Thresholds (Default)

  • Average Response Time: 1000ms
  • P95 Response Time: 2000ms
  • Error Rate: 5%
  • Throughput: 10 req/s

Regression Detection Thresholds

  • Average Response Time: 10% increase
  • P95 Response Time: 15% increase
  • Error Rate: 5% increase
  • Throughput: 10% decrease

Bottleneck Severity Calculation

Severity is calculated based on:

  • Average response time (0-3 points)
  • P95 response time (0-3 points)
  • Error rate (0-3 points)

Capacity Planning

Calculates:

  • Current throughput
  • Maximum safe throughput (based on error rate)
  • Maximum capacity (based on response time)
  • Headroom percentage
  • Scaling recommendations

Scaling Projections

Estimates:

  • Projected throughput based on growth rate
  • Scaling factor needed
  • Recommendations for horizontal/vertical scaling
  • Caching and optimization strategies

API Examples

Create Scenario

curl -X POST http://localhost:3001/api/load-testing/scenarios/create \
  -H "Content-Type: application/json" \
  -d '{
    "name": "payment-flow",
    "description": "Payment processing test",
    "requests": [
      {"method": "GET", "path": "/api/health", "weight": 1},
      {"method": "POST", "path": "/api/stellar/payment/send", "weight": 5}
    ],
    "duration": 120,
    "rampUp": 20,
    "concurrency": 50
  }'

Run Load Test

curl -X POST http://localhost:3001/api/load-testing/run \
  -H "Content-Type: application/json" \
  -d '{"scenarioName": "payment-flow"}'

Check for Regressions

curl -X POST http://localhost:3001/api/load-testing/regression/check \
  -H "Content-Type: application/json" \
  -d '{"scenarioName": "payment-flow"}'

Get Optimization Recommendations

curl -X POST http://localhost:3001/api/load-testing/recommendations \
  -H "Content-Type: application/json" \
  -d '{"scenarioName": "payment-flow"}'

Calculate Capacity

curl -X POST http://localhost:3001/api/load-testing/capacity/calculate \
  -H "Content-Type: application/json" \
  -d '{"scenarioName": "payment-flow", "targetErrorRate": 1}'

Project Scaling Needs

curl -X POST http://localhost:3001/api/load-testing/capacity/project \
  -H "Content-Type: application/json" \
  -d '{"scenarioName": "payment-flow", "growthRate": 0.2, "months": 12}'

CI/CD Integration

Add to your CI/CD pipeline:

# .github/workflows/performance.yml
name: Performance Testing
on: [push, pull_request]

jobs:
  load-test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Run load tests
        run: |
          npm run load-test
      - name: Check regressions
        run: |
          npm run check-regression
      - name: Generate report
        run: |
          npm run performance-report

Testing

Run load testing tests:

npm test -- loadTesting.test.js

Best Practices

  1. Establish Baselines: Create baselines before making changes
  2. Regular Testing: Run load tests regularly (daily/weekly)
  3. Monitor Trends: Track performance over time
  4. Act on Alerts: Investigate and fix critical alerts
  5. Document Changes: Record optimization efforts
  6. Capacity Planning: Plan for growth proactively
  7. Realistic Scenarios: Use realistic user behavior patterns
  8. Gradual Ramp-up: Use ramp-up to simulate real traffic

Performance Optimization Tips

  1. Caching: Implement Redis/Memcached for frequently accessed data
  2. Database: Add indexes, optimize queries, use connection pooling
  3. Async: Use async/await patterns for I/O operations
  4. Batching: Batch requests where possible
  5. Circuit Breakers: Implement circuit breakers for external services
  6. Monitoring: Use APM tools for detailed insights
  7. Scaling: Plan horizontal scaling for high-traffic scenarios
  8. Testing: Conduct regular load tests and regression testing

Future Enhancements

  • Distributed load testing
  • Real-time dashboards
  • Advanced analytics
  • Machine learning-based anomaly detection
  • Integration with monitoring tools
  • Automated scaling recommendations
  • Cost optimization analysis