tests/performance/test_performance_benchmark.py

#!/usr/bin/env python3
"""
Performance benchmarking for search optimizer refactoring.

This script measures the performance impact of the refactoring to ensure
there's no significant performance degradation.
"""

import time
import sys
import statistics
from typing import List, Dict, Any

def benchmark_function(func, *args, iterations=100):
    """Benchmark a function by running it multiple times and measuring performance"""
    
    times = []
    
    # Warm up
    for _ in range(5):
        try:
            func(*args)
        except:
            pass
    
    # Actual benchmarking
    for _ in range(iterations):
        start_time = time.perf_counter()
        try:
            result = func(*args)
            end_time = time.perf_counter()
            times.append(end_time - start_time)
        except Exception as e:
            # Skip failed iterations
            continue
    
    if not times:
        return None
    
    return {
        'mean': statistics.mean(times),
        'median': statistics.median(times),
        'std_dev': statistics.stdev(times) if len(times) > 1 else 0,
        'min': min(times),
        'max': max(times),
        'iterations': len(times)
    }

def test_search_decision_performance():
    """Test performance of search decision functions"""
    
    print("⚡ Performance Testing: Search Decision Functions")
    print("=" * 60)
    
    try:
        from search_optimizer import should_perform_search
        
        # Test cases of varying complexity
        test_cases = [
            {
                "name": "Simple prompt, no history",
                "prompt": "What is machine learning?",
                "history": None
            },
            {
                "name": "Follow-up question with history",
                "prompt": "Tell me more about neural networks",
                "history": [
                    {"user": "What is AI?", "assistant": "AI is artificial intelligence that enables machines to perform tasks that typically require human intelligence..."},
                    {"user": "How does machine learning work?", "assistant": "Machine learning works by training algorithms on data to recognize patterns and make predictions..."}
                ]
            },
            {
                "name": "Complex prompt with extensive history",
                "prompt": "Can you elaborate on the differences between supervised and unsupervised learning approaches?",
                "history": [
                    {"user": "What is AI?", "assistant": "AI is artificial intelligence..."},
                    {"user": "Tell me about machine learning", "assistant": "Machine learning is a subset of AI..."},
                    {"user": "What are neural networks?", "assistant": "Neural networks are computing systems inspired by biological neural networks..."},
                    {"user": "How do deep learning models work?", "assistant": "Deep learning models use multiple layers of neural networks..."}
                ]
            }
        ]
        
        for case in test_cases:
            print(f"\n🔍 Testing: {case['name']}")
            
            # Benchmark the function
            benchmark_result = benchmark_function(
                should_perform_search,
                case["prompt"],
                case["history"],
                iterations=50
            )
            
            if benchmark_result:
                print(f"   ⏱️  Mean time: {benchmark_result['mean']*1000:.2f}ms")
                print(f"   📊 Median time: {benchmark_result['median']*1000:.2f}ms")
                print(f"   📈 Std deviation: {benchmark_result['std_dev']*1000:.2f}ms")
                print(f"   🔄 Iterations: {benchmark_result['iterations']}")
                
                # Performance thresholds
                if benchmark_result['mean'] < 0.01:  # Less than 10ms
                    print("   ✅ Excellent performance")
                elif benchmark_result['mean'] < 0.05:  # Less than 50ms
                    print("   🟡 Good performance")
                else:
                    print("   ⚠️  Performance may need optimization")
            else:
                print("   ❌ Benchmark failed")
        
        return True
        
    except Exception as e:
        print(f"❌ Performance test error: {e}")
        return False

def test_utility_functions_performance():
    """Test performance of utility functions"""
    
    print("\n🛠️  Performance Testing: Utility Functions")
    print("=" * 60)
    
    try:
        from search_optimizer import format_search_context, has_meaningful_conversation_history
        
        # Test format_search_context with different sizes
        small_results = [
            {"source": "Brave", "title": "Test", "body": "Short content"}
        ]
        
        large_results = [
            {
                "source": f"Source{i}",
                "title": f"Long Title {i} with lots of text and information",
                "body": "This is a very long body content that simulates real search results with comprehensive information about various topics including technology, science, and other subjects. " * 10
            }
            for i in range(10)
        ]
        
        print(f"\n🔍 Testing format_search_context (small dataset)")
        small_benchmark = benchmark_function(format_search_context, small_results, iterations=100)
        
        if small_benchmark:
            print(f"   ⏱️  Mean time: {small_benchmark['mean']*1000:.2f}ms")
            print(f"   🔄 Iterations: {small_benchmark['iterations']}")
        
        print(f"\n🔍 Testing format_search_context (large dataset)")
        large_benchmark = benchmark_function(format_search_context, large_results, iterations=50)
        
        if large_benchmark:
            print(f"   ⏱️  Mean time: {large_benchmark['mean']*1000:.2f}ms")
            print(f"   🔄 Iterations: {large_benchmark['iterations']}")
        
        # Test has_meaningful_conversation_history
        print(f"\n🔍 Testing has_meaningful_conversation_history")
        
        complex_history = [
            {"user": f"Question {i}?", "assistant": f"Answer {i} with detailed explanation about the topic."} 
            for i in range(20)
        ]
        
        history_benchmark = benchmark_function(
            has_meaningful_conversation_history, 
            complex_history, 
            iterations=100
        )
        
        if history_benchmark:
            print(f"   ⏱️  Mean time: {history_benchmark['mean']*1000:.2f}ms")
            print(f"   🔄 Iterations: {history_benchmark['iterations']}")
        
        return True
        
    except Exception as e:
        print(f"❌ Utility performance test error: {e}")
        return False

def test_module_import_performance():
    """Test the performance impact of module imports"""
    
    print("\n📦 Performance Testing: Module Import Overhead")
    print("=" * 60)
    
    # Test import time
    import_times = []
    
    for i in range(10):
        start_time = time.perf_counter()
        
        # Simulate fresh import (note: this won't actually re-import due to Python's import cache)
        try:
            import search_optimizer
            end_time = time.perf_counter()
            import_times.append(end_time - start_time)
        except Exception as e:
            print(f"❌ Import error: {e}")
            return False
    
    if import_times:
        avg_import_time = statistics.mean(import_times)
        print(f"⏱️  Average import time: {avg_import_time*1000:.2f}ms")
        
        if avg_import_time < 0.001:  # Less than 1ms
            print("✅ Excellent import performance")
        elif avg_import_time < 0.01:  # Less than 10ms
            print("🟡 Good import performance")
        else:
            print("⚠️  Import time may be higher than expected")
    
    return True

def main():
    """Run performance benchmarking suite"""
    
    print("⚡ Search Optimizer Performance Benchmarking Suite")
    print("=" * 70)
    print("Measuring performance impact of refactoring...")
    print()
    
    tests_passed = 0
    total_tests = 3
    
    # Run performance tests
    if test_search_decision_performance():
        tests_passed += 1
        
    if test_utility_functions_performance():
        tests_passed += 1
        
    if test_module_import_performance():
        tests_passed += 1
    
    # Summary
    print("\n" + "=" * 70)
    print("📊 PERFORMANCE BENCHMARK SUMMARY")
    print("=" * 70)
    
    if tests_passed == total_tests:
        print(f"✅ ALL PERFORMANCE TESTS COMPLETED ({tests_passed}/{total_tests})")
        print("🚀 Performance characteristics within acceptable ranges!")
        print("📈 Refactoring maintains good performance while improving code organization")
        return True
    else:
        print(f"❌ SOME PERFORMANCE TESTS FAILED ({tests_passed}/{total_tests})")
        print("⚠️  Performance may need attention")
        return False

if __name__ == "__main__":
    success = main()
    sys.exit(0 if success else 1)