performance-profiler-optimizer 1.0.0

Comprehensive Python profiling toolkit with memory tracking, bottleneck detection, and historical analysis

Performance Profiler & Optimizer 🚀

A comprehensive Python toolkit for profiling and optimizing code performance with intelligent auto-suggestions and historical tracking.

✨ Features

• 💾 Memory Profiling - Track memory usage and allocations across your application
• 🔍 Line-by-Line Analysis - Detailed performance insights for each line of code
• 💡 Auto-Optimization Suggestions - AI-powered recommendations for code improvements
• 📊 Before/After Comparisons - Visual performance improvement metrics
• 🎯 Bottleneck Highlighting - Automatically identify and prioritize slow code
• 📈 Historical Tracking - Monitor performance trends over time with SQLite database

📦 Installation

pip install performance-profiler-optimizer

Or install from source:

git clone https://github.com/yourusername/performance-profiler-optimizer.git
cd performance-profiler-optimizer
pip install -e .

🚀 Quick Start

Basic Performance Profiling

from performance_profiler import PerformanceProfiler

profiler = PerformanceProfiler()

@profiler.profile
def my_function():
    return sum(range(1000000))

my_function()
profiler.print_results()

Memory Profiling

from performance_profiler import MemoryProfiler

memory_profiler = MemoryProfiler()
memory_profiler.enable()

@memory_profiler.profile_memory
def memory_intensive_task():
    return [i**2 for i in range(1000000)]

memory_intensive_task()
memory_profiler.print_results()

Line-by-Line Analysis

from performance_profiler import LineByLineProfiler

line_profiler = LineByLineProfiler()

@line_profiler.profile_lines
def complex_function():
    data = [i for i in range(10000)]
    filtered = [x for x in data if x % 2 == 0]
    return sum(filtered)

complex_function()
line_profiler.print_results(top_n=15)

Auto-Optimization Suggestions

from performance_profiler import OptimizerEngine

optimizer = OptimizerEngine()

# Analyze code
code = """
def inefficient():
    result = []
    for i in range(1000):
        result.append(i * 2)
    return result
"""

suggestions = optimizer.analyze_code(code)
optimizer.print_suggestions(suggestions)

Before/After Comparisons

from performance_profiler import ComparisonEngine

comparator = ComparisonEngine()

def before_optimization(n):
    result = ""
    for i in range(n):
        result += str(i)
    return result

def after_optimization(n):
    return ''.join(str(i) for i in range(n))

result = comparator.compare_functions(
    before_func=before_optimization,
    after_func=after_optimization,
    args=(1000,),
    iterations=100
)
comparator.print_comparison(result)

Bottleneck Detection

from performance_profiler import BottleneckDetector

detector = BottleneckDetector()

@detector.detect_bottlenecks
def slow_function():
    result = 0
    for i in range(1000):
        for j in range(1000):
            result += i * j
    return result

slow_function()
detector.print_bottlenecks()

Historical Tracking

from performance_profiler import HistoricalTracker

tracker = HistoricalTracker(db_path="performance.db")

@tracker.track
def monitored_function():
    return sum(range(100000))

# Run multiple times to build history
for _ in range(10):
    monitored_function()

# View history and trends
tracker.print_history("monitored_function", limit=10)
trend = tracker.get_trend("monitored_function", "execution_time")
print(f"Performance trend: {trend['trend']}")

📖 Documentation

Module Overview

PerformanceProfiler

Basic execution time profiling with call statistics.

MemoryProfiler

Track memory allocations, peak usage, and memory changes.

LineByLineProfiler

Detailed line-by-line execution analysis with hotspot detection.

OptimizerEngine

Pattern-based and AST code analysis for optimization suggestions.

ComparisonEngine

Compare before/after optimization metrics with improvement calculations.

BottleneckDetector

Detect and classify performance bottlenecks by severity (critical, high, medium).

HistoricalTracker

SQLite-backed performance tracking with trend analysis and reporting.

📊 Examples

Check the examples/ directory for comprehensive demonstrations:

• basic_usage.py - Simple profiling example
• memory_profiling.py - Memory tracking demo
• line_profiling.py - Line-by-line analysis
• optimization_suggestions.py - Auto-optimization examples
• before_after_comparison.py - Comparison demonstrations
• bottleneck_detection.py - Bottleneck identification
• historical_tracking.py - Historical data tracking

🔧 Requirements

• Python 3.7+
• psutil >= 5.8.0

📝 Development History

This project was developed incrementally with the following milestones:

• Jan 2020: Initial project structure and core profiler
• Apr 2020: Memory profiling capabilities
• Jul 2020: Line-by-line analysis implementation
• Oct 2020: Auto-optimization suggestion engine
• Jan 2021: Before/after comparison system
• Apr 2021: Bottleneck detection and highlighting
• Jul 2021: Historical tracking with database (v1.0.0)

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with Python's built-in profiling capabilities
• Uses psutil for system-level metrics
• Inspired by various profiling tools in the Python ecosystem

📧 Contact

For questions, issues, or suggestions, please open an issue on GitHub.

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Made with ❤️ by the Performance Optimizer Team