Performance & Speed Issues

Performance & Speed Issues

Slow workflows can be frustrating and may cause timeouts or browser crashes. This guide helps you identify performance bottlenecks and optimize workflow execution speed.

⚡ Quick Performance Fixes

Try these first:

• 🚀 Close unnecessary browser tabs - Reduces memory pressure
• 🚀 Restart your browser - Clears memory leaks and resets performance
• 🚀 Update your browser - Latest versions have performance improvements
• 🚀 Disable other extensions - Reduces resource competition
• 🚀 Check available RAM - Ensure sufficient system memory

🐌 Common Performance Problems

Slow Content Extraction

Large Page Content

Symptoms:

• Workflows take minutes to extract simple data
• Browser becomes unresponsive during extraction
• “Page unresponsive” warnings appear

Performance comparison:

Content Size	Extraction Time	Memory Usage	Recommended Action
< 1MB	< 2 seconds	Low	No optimization needed
1-5MB	2-10 seconds	Medium	Use CSS selectors
5-20MB	10-30 seconds	High	Extract in chunks
> 20MB	> 30 seconds	Very High	Limit extraction scope

Optimization strategies:

// Instead of extracting all content
const allText = document.body.innerText; // Slow for large pages

// Use targeted selectors
const specificContent = document.querySelectorAll('.article-content p');
const limitedContent = Array.from(specificContent)
  .slice(0, 50) // Limit to first 50 paragraphs
  .map(p => p.textContent);

Multiple Extraction Operations

Problem: Running many extraction nodes simultaneously

Solution - Sequential Processing:

graph LR
    A[Page Load] --> B[Extract Headers]
    B --> C[Extract Content]
    C --> D[Extract Links]
    D --> E[Process Results]

    style A fill:#e3f2fd
    style B fill:#e8f5e8
    style C fill:#e8f5e8
    style D fill:#e8f5e8
    style E fill:#f3e5f5

Optimization techniques:

Approach	Speed	Memory	Best For
Single extraction	Fastest	Lowest	Simple data
Batched extraction	Medium	Medium	Multiple similar elements
Parallel extraction	Variable	Highest	Independent data sources
Streaming extraction	Consistent	Low	Large datasets

AI Processing Bottlenecks

Slow AI Analysis

Common causes:

• Large input text sent to AI models
• Complex prompts requiring extensive processing
• Multiple AI calls in sequence
• Using slower AI models

Optimization strategies:

Problem	Cause	Solution
Large text input	Sending entire page content	Summarize or chunk text first
Complex prompts	Overly detailed instructions	Simplify and focus prompts
Sequential AI calls	Waiting for each response	Batch similar requests
Slow model choice	Using most capable but slowest model	Use faster models for simple tasks

Text chunking example:

// Instead of processing entire article
const fullArticle = document.querySelector('.article').textContent; // 10,000+ words

// Chunk into manageable pieces
function chunkText(text, maxWords = 500) {
  const words = text.split(' ');
  const chunks = [];

  for (let i = 0; i < words.length; i += maxWords) {
    chunks.push(words.slice(i, i + maxWords).join(' '));
  }

  return chunks;
}

const chunks = chunkText(fullArticle, 300); // Process 300 words at a time

Browser Resource Issues

Memory Consumption

Symptoms:

• Browser becomes sluggish during workflow execution
• “Out of memory” errors
• System becomes unresponsive

Memory monitoring:

// Check memory usage (Chrome only)
if (performance.memory) {
  const memInfo = {
    used: Math.round(performance.memory.usedJSHeapSize / 1024 / 1024) + ' MB',
    total: Math.round(performance.memory.totalJSHeapSize / 1024 / 1024) + ' MB',
    limit: Math.round(performance.memory.jsHeapSizeLimit / 1024 / 1024) + ' MB'
  };
  console.log('Memory usage:', memInfo);
}

Memory optimization:

Technique	Impact	Implementation
Clear variables	Medium	Set large objects to null after use
Limit DOM queries	High	Cache DOM elements, avoid repeated queries
Process in batches	High	Break large operations into smaller chunks
Use streaming	Very High	Process data as it arrives, don’t store all

CPU Intensive Operations

Symptoms:

• High CPU usage during workflow execution
• Browser UI becomes unresponsive
• Fan noise increases significantly

CPU optimization techniques:

// Use requestAnimationFrame for heavy processing
function processLargeDataset(data, callback) {
  let index = 0;
  const batchSize = 100;

  function processBatch() {
    const endIndex = Math.min(index + batchSize, data.length);

    // Process batch
    for (let i = index; i < endIndex; i++) {
      // Process data[i]
    }

    index = endIndex;

    if (index < data.length) {
      // Continue processing in next frame
      requestAnimationFrame(processBatch);
    } else {
      // Processing complete
      callback();
    }
  }

  processBatch();
}

🔧 Performance Diagnostic Tools

Workflow Timing Analysis

Add timing measurements:

// Measure individual node performance
const nodeTimings = {};

function measureNode(nodeName, operation) {
  const startTime = performance.now();

  const result = operation();

  const endTime = performance.now();
  nodeTimings[nodeName] = endTime - startTime;

  console.log(`${nodeName} took ${endTime - startTime}ms`);
  return result;
}

// Usage example
const extractedData = measureNode('ContentExtraction', () => {
  return document.querySelectorAll('.content p');
});

Browser Performance Monitoring

Performance API usage:

// Monitor overall workflow performance
performance.mark('workflow-start');

// ... workflow execution ...

performance.mark('workflow-end');
performance.measure('workflow-duration', 'workflow-start', 'workflow-end');

// Get measurements
const measures = performance.getEntriesByType('measure');
measures.forEach(measure => {
  console.log(`${measure.name}: ${measure.duration}ms`);
});

Resource Usage Tracking

Monitor resource consumption:

// Track resource usage over time
const resourceMonitor = {
  startTime: Date.now(),
  measurements: [],

  measure() {
    const now = Date.now();
    const measurement = {
      timestamp: now - this.startTime,
      memory: performance.memory ? {
        used: performance.memory.usedJSHeapSize,
        total: performance.memory.totalJSHeapSize
      } : null,
      timing: performance.now()
    };

    this.measurements.push(measurement);
    return measurement;
  },

  report() {
    console.table(this.measurements);
  }
};

// Use during workflow
resourceMonitor.measure(); // Before workflow
// ... workflow execution ...
resourceMonitor.measure(); // After workflow
resourceMonitor.report();

🎯 Optimization Strategies

Content Extraction Optimization

Smart Selector Usage

Efficient selectors:

// Slow - searches entire document
const slowQuery = document.querySelectorAll('*');

// Fast - specific and targeted
const fastQuery = document.querySelectorAll('.article-content p');

// Faster - use IDs when available
const fastestQuery = document.getElementById('main-content');

Selector performance ranking:

1. 🟢 ID selectors (#content) - Fastest
2. 🟡 Class selectors (.article) - Fast
3. 🟡 Tag selectors (p, div) - Medium
4. 🟠 Attribute selectors ([data-id]) - Slower
5. 🔴 Complex selectors (div > p:nth-child(2)) - Slowest

Batch Processing

Process data in chunks:

// Instead of processing all at once
function processAllData(data) {
  return data.map(item => expensiveOperation(item)); // Blocks browser
}

// Process in batches with delays
async function processBatched(data, batchSize = 50) {
  const results = [];

  for (let i = 0; i < data.length; i += batchSize) {
    const batch = data.slice(i, i + batchSize);
    const batchResults = batch.map(item => expensiveOperation(item));
    results.push(...batchResults);

    // Allow browser to update UI
    await new Promise(resolve => setTimeout(resolve, 10));
  }

  return results;
}

AI Processing Optimization

Prompt Optimization

Efficient prompts:

// Slow - overly complex prompt
const slowPrompt = `
Please analyze this text in great detail, considering all possible interpretations,
cultural contexts, linguistic nuances, and provide a comprehensive analysis
including sentiment, themes, key points, and recommendations...
`;

// Fast - focused and specific
const fastPrompt = `
Extract the main topic and sentiment (positive/negative/neutral) from this text:
`;

Model Selection

Choose appropriate models:

Task Type	Recommended Model	Speed	Accuracy
Simple classification	Fast/Small models	Very Fast	Good
Text summarization	Medium models	Fast	Very Good
Complex analysis	Large models	Slow	Excellent
Code generation	Specialized models	Medium	Excellent

Workflow Architecture Optimization

Parallel vs Sequential Processing

Sequential (slower but safer):

graph LR
    A[Extract Text] --> B[Process Text]
    B --> C[Analyze Sentiment]
    C --> D[Generate Summary]
    D --> E[Save Results]

Parallel (faster but more complex):

graph TB
    A[Extract Text] --> B[Process Text]
    B --> C[Analyze Sentiment]
    B --> D[Generate Summary]
    B --> E[Extract Keywords]
    C --> F[Combine Results]
    D --> F
    E --> F
    F --> G[Save Results]

Caching Strategies

Implement smart caching:

// Cache expensive operations
const cache = new Map();

function cachedOperation(input) {
  const cacheKey = JSON.stringify(input);

  if (cache.has(cacheKey)) {
    console.log('Cache hit');
    return cache.get(cacheKey);
  }

  console.log('Cache miss - computing...');
  const result = expensiveOperation(input);
  cache.set(cacheKey, result);

  return result;
}

📊 Performance Benchmarking

Workflow Performance Targets

Acceptable performance ranges:

Operation Type	Target Time	Warning Threshold	Action Required
Simple extraction	< 2 seconds	> 5 seconds	Optimize selectors
Complex extraction	< 10 seconds	> 30 seconds	Reduce scope
AI processing	< 15 seconds	> 60 seconds	Optimize prompts
Data transformation	< 5 seconds	> 20 seconds	Batch processing
File operations	< 3 seconds	> 10 seconds	Check file size

Performance Testing

Create performance tests:

// Performance test suite
const performanceTests = {
  async testExtraction() {
    const start = performance.now();
    const data = document.querySelectorAll('.test-content');
    const end = performance.now();

    return {
      operation: 'Content Extraction',
      duration: end - start,
      dataSize: data.length,
      passed: (end - start) < 2000 // 2 second threshold
    };
  },

  async testProcessing(data) {
    const start = performance.now();
    const processed = data.map(item => item.textContent.trim());
    const end = performance.now();

    return {
      operation: 'Data Processing',
      duration: end - start,
      itemCount: processed.length,
      passed: (end - start) < 5000 // 5 second threshold
    };
  }
};

// Run performance tests
async function runPerformanceTests() {
  const results = [];

  results.push(await performanceTests.testExtraction());
  // Add more tests...

  console.table(results);
  return results;
}

🚨 Performance Warning Signs

Early Warning Indicators

Watch for these signs:

• ⚠️ Extraction taking > 10 seconds - Content too large or selectors inefficient
• ⚠️ Memory usage > 500MB - Potential memory leak or excessive data storage
• ⚠️ CPU usage > 80% - Processing too intensive, needs optimization
• ⚠️ Browser becoming unresponsive - Operations blocking UI thread
• ⚠️ Frequent timeouts - Network issues or processing bottlenecks

Performance Degradation Patterns

Common degradation causes:

Pattern	Cause	Solution
Gradual slowdown	Memory leaks	Restart browser, optimize memory usage
Sudden performance drop	Resource exhaustion	Reduce batch sizes, add delays
Inconsistent timing	Network variability	Add retry logic, optimize requests
Progressive failure	Accumulating errors	Implement error recovery, reset state

🔄 Ongoing Performance Maintenance

Regular Performance Monitoring

Monthly checklist:

• Review workflow execution times
• Check browser memory usage patterns
• Update to latest browser version
• Clean browser cache and data
• Review and optimize slow workflows

Performance Optimization Workflow

Systematic optimization process:

1. Identify bottlenecks - Use timing and profiling tools
2. Prioritize improvements - Focus on biggest impact first
3. Implement optimizations - Make targeted changes
4. Test and validate - Ensure improvements work
5. Monitor results - Track performance over time

Best Practices for Fast Workflows

Design principles:

• 🎯 Be specific - Use targeted selectors and focused operations
• 🔄 Process incrementally - Break large operations into smaller chunks
• 💾 Cache intelligently - Store expensive computation results
• ⚡ Optimize early - Consider performance from the start
• 📊 Measure everything - Track performance metrics consistently