Performance monitoring solution

The Necessity of Engineering Standards

The complexity of modern front-end projects continues to increase, and team collaboration has become the norm. Engineering standards serve as the cornerstone for ensuring project quality. The lack of unified standards can lead to issues such as chaotic code styles, difficulties in tracking performance problems, and reduced collaboration efficiency. Taking React projects as an example, different developers might mix class components and function components, or even use multiple state management solutions within the same project. Such inconsistencies significantly increase maintenance costs.

// Non-standard example: Mixing multiple styles
class OldComponent extends React.Component {
  // ...
}

const NewComponent = () => {
  // ...
}

// Standard example: Unified use of function components
const ComponentA = () => {
  // ...
}

const ComponentB = () => {
  // ...
}

Building a Code Standard System

A complete code standard should encompass multiple dimensions: coding style, directory structure, component design, etc. The combination of ESLint + Prettier has become an industry standard, but it requires customized configuration based on team characteristics. While the Airbnb standard is comprehensive, it may be overly strict. It is recommended to start with basic rules and gradually expand.

// Example of .eslintrc.js
module.exports = {
  extends: [
    'eslint:recommended',
    'plugin:react/recommended',
    'plugin:@typescript-eslint/recommended'
  ],
  rules: {
    'react/prop-types': 'off',
    '@typescript-eslint/explicit-module-boundary-types': 'off',
    'indent': ['error', 2]
  }
}

Directory structure standards need to balance flexibility and constraints. A modular structure organized by functionality is more aligned with modern front-end engineering principles than one organized by type:

src/
├── features/       # Functional modules
│   ├── auth/       # Authentication-related
│   ├── dashboard/  # Dashboard
├── lib/            # Public libraries
├── app/            # Application entry

Performance Monitoring Indicator System

A comprehensive performance monitoring system should cover the following core metrics:

1. Loading Performance: Web Vitals metrics such as FP/FCP/LCP
2. Runtime Performance: FPS, long task ratio, memory usage
3. Interaction Performance: Click response latency, scroll smoothness
4. Exception Monitoring: JS error rate, resource loading failure rate

The Performance API can be used to obtain precise metric data:

// Measuring key lifecycle stages
const measurePerf = () => {
  const [entry] = performance.getEntriesByName('first-contentful-paint');
  console.log('FCP:', entry.startTime);
  
  // Custom metrics
  performance.mark('custom:start');
  // ...Business logic
  performance.mark('custom:end');
  performance.measure('custom', 'custom:start', 'custom:end');
}

Implementing Automated Monitoring Solutions

A monitoring system requires end-to-end automation for data collection, reporting, and analysis. While commercial solutions like Sentry are ready-to-use, building a custom solution offers greater flexibility. Example of real-time monitoring based on PerformanceObserver:

const perfObserver = new PerformanceObserver((list) => {
  list.getEntries().forEach(entry => {
    if (entry.entryType === 'longtask') {
      reportLongTask(entry);
    }
  });
});

perfObserver.observe({ entryTypes: ['longtask'] });

// Error monitoring
window.addEventListener('error', (e) => {
  sendError({
    message: e.message,
    stack: e.error.stack,
    filename: e.filename,
    lineno: e.lineno
  });
});

Performance Benchmarking Strategy

Establishing performance baselines is a prerequisite for monitoring. Lighthouse CI can be used to automate testing in the CI pipeline:

# .github/workflows/lighthouse.yml
name: Lighthouse Audit
on: [push]
jobs:
  lighthouse:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - uses: actions/setup-node@v2
      - run: npm install
      - run: npm run build
      - uses: treosh/lighthouse-ci-action@v8
        with:
          urls: |
            http://localhost:5000/
            http://localhost:5000/dashboard
          budgetPath: ./lighthouse-budget.json

Example of a benchmarking configuration file:

// lighthouse-budget.json
{
  "performance": {
    "first-contentful-paint": "<=1.5s",
    "interactive": "<=3s",
    "speed-index": "<=3s"
  }
}

Exception Tracking and Diagnosis

Exception tracking requires contextual information for effective diagnosis. Build error reporting with rich context:

function captureError(error, context = {}) {
  const errorData = {
    timestamp: Date.now(),
    message: error.message,
    stack: error.stack,
    user: currentUser,
    route: window.location.pathname,
    device: {
      type: /Mobile/.test(navigator.userAgent) ? 'mobile' : 'desktop',
      memory: navigator.deviceMemory
    },
    context
  };
  
  sendToAnalytics(errorData);
}

// Usage example
try {
  riskyOperation();
} catch (err) {
  captureError(err, {
    component: 'CheckoutForm',
    state: store.getState()
  });
}

Closed-Loop Performance Optimization Process

Monitoring data must translate into concrete optimization measures to deliver value. Establish a complete loop from alerting to resolution:

1. Threshold Alerts: Set intelligent thresholds for core metrics
2. Root Cause Analysis: Correlate multiple metrics for issue identification
3. Optimization Implementation: Guide optimization direction based on data
4. Effect Validation: Verify optimization results through A/B testing

Example alert rule configuration:

// Monitoring rule configuration
const rules = {
  lcp: {
    threshold: 2500,
    consecutive: 3,
    action: 'alert'
  },
  jsErrorRate: {
    threshold: 0.01,
    window: '1h',
    action: 'page'
  }
};

Building a Visual Monitoring Platform

Data visualization is a critical tool for performance analysis. Use libraries like ECharts to build custom dashboards:

// Using ECharts to draw performance trends
const initChart = () => {
  const chart = echarts.init(document.getElementById('perf-chart'));
  chart.setOption({
    tooltip: { trigger: 'axis' },
    xAxis: { type: 'category', data: timestamps },
    yAxis: { type: 'value' },
    series: [{
      name: 'LCP',
      type: 'line',
      data: lcpValues,
      markLine: {
        data: [{ type: 'average', name: 'Avg' }]
      }
    }]
  });
};

A typical monitoring dashboard should include:

• Real-time metric dashboards
• Historical trend comparisons
• Exception event timelines
• User distribution heatmaps

Quality Gates in Continuous Integration

Incorporate performance metrics into CI pipeline quality gates to block code merges that cause performance regressions:

# Integrating performance testing in CI
steps:
  - name: Run tests
    run: npm test
  
  - name: Performance budget
    run: |
      lighthouse http://localhost:5000 \
        --output=json \
        --budget-path=./budget.json
  
  - name: Assert metrics
    run: |
      if ! jq '.audits["first-contentful-paint"].numericValue <= 1500' results.json; then
        echo "FCP exceeds budget"
        exit 1
      fi

Correlation Analysis of User Experience Metrics

Correlate technical metrics with business metrics, such as:

• The relationship between LCP and conversion rates
• The impact of interaction latency on user dwell time
• The correlation between error rates and user churn

// Example of correlation analysis
function analyzeCorrelation(perfData, businessData) {
  const lcpValues = perfData.map(d => d.lcp);
  const conversionRates = businessData.map(d => d.conversion);
  
  // Use statistical methods to calculate correlation coefficients
  return calculatePearson(lcpValues, conversionRates);
}

Specialized Optimization Monitoring for Mobile

Mobile requires additional focus on:

• Performance on low-end devices
• Behavior in weak network conditions
• Memory usage
• Battery consumption impact

Use the Device Memory API for differentiated monitoring:

// Adjust monitoring strategy based on device memory
const memory = navigator.deviceMemory || 4;

if (memory < 2) {
  startEnhancedMonitoring();
  throttleHeavyOperations();
}

Front-End Cache Strategy Monitoring

Cache hit rates directly impact performance. Monitor:

• CDN cache efficiency
• Service Worker cache strategies
• Local storage usage

// Monitoring Service Worker cache
navigator.serviceWorker.addEventListener('message', event => {
  if (event.data.type === 'CACHE_METRICS') {
    reportCacheStats({
      hitRate: event.data.hitRate,
      staleRate: event.data.staleRate
    });
  }
});

Build Output Analysis and Monitoring

Modern front-end builds require monitoring:

• Bundle size trends
• Dependency count and proportion
• Duplicate code detection
• Tree-shaking effectiveness

Use Webpack Stats Analyzer for analysis:

// webpack.config.js
module.exports = {
  plugins: [
    new BundleAnalyzerPlugin({
      analyzerMode: 'static',
      reportFilename: 'bundle-report.html'
    })
  ]
};

Building a Performance Optimization Pattern Library

Establish a reusable performance optimization pattern library:

• Virtual scrolling implementations
• Best practices for lazy loading images
• Code splitting strategies
• Data prefetching solutions

// Example of a lazy loading image component
const LazyImage = ({ src, alt }) => {
  const [isLoaded, setIsLoaded] = useState(false);
  
  useEffect(() => {
    const img = new Image();
    img.src = src;
    img.onload = () => setIsLoaded(true);
    
    return () => {
      img.onload = null;
    };
  }, [src]);

  return (
    <div className="lazy-image">
      {isLoaded ? (
        <img src={src} alt={alt} />
      ) : (
        <Placeholder />
      )}
    </div>
  );
};

Observability Design for Monitoring Systems

The monitoring system itself requires observability:

• Data collection coverage
• Reporting success rate monitoring
• Data processing latency
• Storage utilization

// Monitoring system health check
function monitorSystemHealth() {
  setInterval(() => {
    const health = {
      queueSize: reportingQueue.length,
      lastFlush: Date.now() - lastFlushTime,
      errorRate: errorCount / totalCount
    };
    sendSystemMetrics(health);
  }, 60000);
}

Gradual Rollout Mechanism for Performance Optimizations

Performance optimizations require progressive rollout:

1. Small-scale A/B testing to validate effects
2. Gradually expand coverage
3. Real-time monitoring of core metrics
4. Fast rollback mechanisms

// Feature flag control
const featureFlags = {
  newOptimization: {
    enabled: isUserInExperiment(experimentId),
    rollout: 0.2 // 20% traffic
  }
};

if (featureFlags.newOptimization.enabled) {
  useOptimizedImplementation();
} else {
  useLegacyImplementation();
}

Integration of Performance Monitoring with Business Alerts

Integrate front-end monitoring with existing alerting systems:

• Connect to IM tools like Slack/DingTalk
• Tiered alerting strategies
• On-call response mechanisms
• Automatic ticket creation

// Example of alert integration
function triggerAlert(metric, value, threshold) {
  const message = `[Frontend Alert] ${metric} current value ${value} exceeds threshold ${threshold}`;
  
  // Send to Slack
  postToSlack('#alerts', message);
  
  // Create a ticket for critical alerts
  if (severity > 3) {
    createJiraTicket({
      title: `[CRITICAL] ${metric} anomaly`,
      description: message
    });
  }
}

Long-Term Performance Trend Analysis

Establish performance archives to analyze long-term trends:

• Compare metrics monthly/quarterly
• Identify seasonal patterns
• Predict capacity requirements
• Evaluate the effects of technical decisions

-- Example of performance data aggregation query
SELECT 
  DATE_TRUNC('month', timestamp) AS month,
  AVG(lcp) AS avg_lcp,
  PERCENTILE_CONT(0.9) WITHIN GROUP (ORDER BY lcp) AS p90_lcp
FROM performance_metrics
GROUP BY 1
ORDER BY 1;

Impact Assessment of New Technologies on Performance

Evaluate the performance impact before adopting new technologies:

• Use case analysis for WebAssembly
• Performance testing of new CSS features
• Compatibility costs of new JavaScript syntax
• Performance trade-offs in framework upgrades

// WASM performance comparison test
async function runBenchmark() {
  const jsStart = performance.now();
  runJSAlgorithm();
  const jsTime = performance.now() - jsStart;
  
  const wasmModule = await WebAssembly.instantiateStreaming(fetch('algorithm.wasm'));
  const wasmStart = performance.now();
  wasmModule.exports.runAlgorithm();
  const wasmTime = performance.now() - wasmStart;
  
  return { jsTime, wasmTime };
}