wehub-resource-sync
98 lines
4.9 KiB
Python
98 lines
4.9 KiB
Python
"""
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Compare performance before and after optimizations
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"""
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def read_baseline():
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"""Read baseline performance metrics"""
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with open('performance_baseline.txt', 'r') as f:
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content = f.read()
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# Extract key metrics
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metrics = {}
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lines = content.split('\n')
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for i, line in enumerate(lines):
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if 'Total Time:' in line:
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metrics['total_time'] = float(line.split(':')[1].strip().split()[0])
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elif 'Memory Used:' in line:
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metrics['memory_mb'] = float(line.split(':')[1].strip().split()[0])
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elif 'validate_coverage:' in line and i+1 < len(lines) and 'Avg Time:' in lines[i+2]:
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metrics['validate_coverage_ms'] = float(lines[i+2].split(':')[1].strip().split()[0])
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elif 'select_links:' in line and i+1 < len(lines) and 'Avg Time:' in lines[i+2]:
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metrics['select_links_ms'] = float(lines[i+2].split(':')[1].strip().split()[0])
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elif 'calculate_confidence:' in line and i+1 < len(lines) and 'Avg Time:' in lines[i+2]:
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metrics['calculate_confidence_ms'] = float(lines[i+2].split(':')[1].strip().split()[0])
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return metrics
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def print_comparison(before_metrics, after_metrics):
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"""Print performance comparison"""
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print("\n" + "="*80)
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print("PERFORMANCE COMPARISON: BEFORE vs AFTER OPTIMIZATIONS")
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print("="*80)
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# Total time
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time_improvement = (before_metrics['total_time'] - after_metrics['total_time']) / before_metrics['total_time'] * 100
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print(f"\n📊 Total Time:")
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print(f" Before: {before_metrics['total_time']:.2f} seconds")
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print(f" After: {after_metrics['total_time']:.2f} seconds")
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print(f" Improvement: {time_improvement:.1f}% faster ✅" if time_improvement > 0 else f" Slower: {-time_improvement:.1f}% ❌")
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# Memory
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mem_improvement = (before_metrics['memory_mb'] - after_metrics['memory_mb']) / before_metrics['memory_mb'] * 100
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print(f"\n💾 Memory Usage:")
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print(f" Before: {before_metrics['memory_mb']:.2f} MB")
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print(f" After: {after_metrics['memory_mb']:.2f} MB")
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print(f" Improvement: {mem_improvement:.1f}% less memory ✅" if mem_improvement > 0 else f" More memory: {-mem_improvement:.1f}% ❌")
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# Key operations
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print(f"\n⚡ Key Operations:")
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# Validate coverage
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if 'validate_coverage_ms' in before_metrics and 'validate_coverage_ms' in after_metrics:
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val_improvement = (before_metrics['validate_coverage_ms'] - after_metrics['validate_coverage_ms']) / before_metrics['validate_coverage_ms'] * 100
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print(f"\n validate_coverage:")
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print(f" Before: {before_metrics['validate_coverage_ms']:.1f} ms")
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print(f" After: {after_metrics['validate_coverage_ms']:.1f} ms")
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print(f" Improvement: {val_improvement:.1f}% faster ✅" if val_improvement > 0 else f" Slower: {-val_improvement:.1f}% ❌")
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# Select links
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if 'select_links_ms' in before_metrics and 'select_links_ms' in after_metrics:
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sel_improvement = (before_metrics['select_links_ms'] - after_metrics['select_links_ms']) / before_metrics['select_links_ms'] * 100
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print(f"\n select_links:")
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print(f" Before: {before_metrics['select_links_ms']:.1f} ms")
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print(f" After: {after_metrics['select_links_ms']:.1f} ms")
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print(f" Improvement: {sel_improvement:.1f}% faster ✅" if sel_improvement > 0 else f" Slower: {-sel_improvement:.1f}% ❌")
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# Calculate confidence
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if 'calculate_confidence_ms' in before_metrics and 'calculate_confidence_ms' in after_metrics:
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calc_improvement = (before_metrics['calculate_confidence_ms'] - after_metrics['calculate_confidence_ms']) / before_metrics['calculate_confidence_ms'] * 100
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print(f"\n calculate_confidence:")
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print(f" Before: {before_metrics['calculate_confidence_ms']:.1f} ms")
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print(f" After: {after_metrics['calculate_confidence_ms']:.1f} ms")
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print(f" Improvement: {calc_improvement:.1f}% faster ✅" if calc_improvement > 0 else f" Slower: {-calc_improvement:.1f}% ❌")
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print("\n" + "="*80)
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# Overall assessment
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if time_improvement > 50:
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print("🎉 EXCELLENT OPTIMIZATION! More than 50% performance improvement!")
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elif time_improvement > 30:
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print("✅ GOOD OPTIMIZATION! Significant performance improvement!")
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elif time_improvement > 10:
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print("👍 DECENT OPTIMIZATION! Noticeable performance improvement!")
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else:
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print("🤔 MINIMAL IMPROVEMENT. Further optimization may be needed.")
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print("="*80)
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if __name__ == "__main__":
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# Example usage - you'll run this after implementing optimizations
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baseline = read_baseline()
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print("Baseline metrics loaded:")
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for k, v in baseline.items():
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print(f" {k}: {v}")
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print("\n⚠️ Run the performance test again after optimizations to compare!")
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print("Then update this script with the new metrics to see the comparison.") |