Build System Profiler

A sophisticated web-based dashboard for monitoring and analyzing build system performance. This tool provides real-time metrics collection, statistical analysis, and anomaly detection to help teams optimize their build processes and identify performance issues.

Key Features

Performance Monitoring

• Real-time build metrics collection
• Statistical analysis of build patterns
• Automated anomaly detection
• Build time trend analysis
• Resource utilization tracking
• Success rate monitoring

Analytics Dashboard

• Interactive performance visualizations
• Detailed build metrics
• Customizable time ranges
• Filterable data views
• Export capabilities
• Real-time updates

Anomaly Detection

• Statistical deviation analysis
• Pattern-based detection
• Configurable thresholds
• Automated alerts
• Historical comparison
• Detailed anomaly reports

Technical Requirements

System Requirements

• Python 3.8+
• Modern web browser
• 2GB RAM minimum
• 500MB disk space

Supported Build Systems

• Bazel (current)
• CMake (planned)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/build-system-profiler.git
cd build-system-profiler

2. Set up virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

Quick Start

1. Start the dashboard:

python -m dashboard.app

2. Access the web interface:

http://localhost:5000

3. Configure your project:

• Navigate to Settings
• Set project path
• Select build system
• Configure thresholds

Configuration

Dashboard Settings

{
    "host": "localhost",
    "port": 5000,
    "debug": false,
    "metrics_retention_days": 30,
    "anomaly_threshold": 1.5
}

Build System Configuration

{
    "build_system": "bazel",
    "workspace_path": "/path/to/workspace",
    "collection_interval": 60,
    "max_builds": 1000
}

API Reference

Metrics Collection

from collector import MetricsCollector

collector = MetricsCollector()
collector.start()
collector.collect_build_metrics()
collector.stop()

Performance Analysis

from analyzer import PerformanceAnalyzer

analyzer = PerformanceAnalyzer()
results = analyzer.analyze_build_metrics(metrics_data)
anomalies = analyzer.detect_anomalies(results)

Development

Setting Up Development Environment

1. Install development dependencies:

pip install -e ".[dev]"

2. Install pre-commit hooks:

pre-commit install

Running Tests

pytest tests/
pytest tests/ --cov=src/

Code Style

• Follow PEP 8 guidelines
• Use type hints
• Maintain test coverage above 80%
• Document all public APIs

Security

• HTTPS for all connections
• Input validation
• Secure data storage
• Regular security updates
• Access control implementation

Performance

Metrics Collection

• Minimal build impact (<1%)
• Efficient data storage
• Optimized queries
• Background processing

Dashboard Performance

• Fast page loads (<2s)
• Efficient data updates
• Responsive interface
• Optimized database queries

Support

• GitHub Issues for bug reports
• Feature requests welcome
• Documentation updates
• Community contributions

License

MIT License - See LICENSE file for details

Acknowledgments

• Build system communities
• Open source contributors
• Testing volunteers
• Feature requestors