InsightLogger

A comprehensive logging and monitoring library for Python applications with performance tracking, system monitoring, and analytics capabilities.

Installation

pip install insightlog

Quick Start

from insightlog import InsightLogger

# Basic setup
logger = InsightLogger(name="MyApp")

# Log messages
logger.log_types("INFO", "Application started")
logger.log_types("ERROR", "Something went wrong")

# Track function performance
@logger.log_function_time
def my_function():
    # Your code here
    pass

# View insights
logger.view_insights()

Features

Core Logging

• Multiple log levels (INFO, DEBUG, ERROR, SUCCESS, WARNING, CRITICAL, etc.)
• Colored console output
• File logging with rotation
• Contextual logging with metadata and tags

Performance Monitoring

• Function execution time tracking
• Memory usage monitoring
• System resource monitoring (CPU, memory, network)
• Custom metrics tracking

Analytics & Reporting

• Performance profiling and bottleneck identification
• Anomaly detection
• Health scoring
• HTML dashboard generation
• Data export (JSON, CSV)

Advanced Features

• Database logging (SQLite)
• Email alerts for critical events
• Security event logging
• Plugin system for extensibility

Configuration

Basic Configuration

logger = InsightLogger(
    name="MyApp",
    enable_monitoring=True,    # Enable system monitoring
    enable_database=True,      # Enable database logging
    enable_alerts=False        # Disable email alerts
)

Customizable Tracking Options

You can selectively enable/disable specific tracking features:

logger = InsightLogger(
    name="MyApp",
    # Core settings
    save_log="enabled",           # File logging: "enabled" or "disabled"
    log_level=logging.INFO,       # Log level
    
    # Monitoring options
    enable_monitoring=True,       # System resource monitoring
    enable_database=True,         # Database logging
    enable_alerts=False,          # Email alerts
    
    # File settings
    log_dir=".insight",          # Log directory
    max_bytes=1000000,           # Max log file size
    backup_count=3               # Number of backup files
)

Alert Configuration

# Enable email alerts
logger = InsightLogger(
    name="MyApp",
    enable_alerts=True,
    alert_email="[email protected]",
    smtp_server="smtp.gmail.com",
    smtp_port=587,
    smtp_user="[email protected]",
    smtp_password="your_password"
)

# Customize alert thresholds
logger.alert_thresholds = {
    'cpu_usage': 80,        # CPU percentage
    'memory_usage': 85,     # Memory percentage
    'error_rate': 10,       # Error rate percentage
    'response_time': 5000   # Response time in ms
}

Usage Examples

Performance Tracking

# Method 1: Decorator
@logger.log_function_time
def process_data():
    # Your code here
    pass

# Method 2: Context manager
with logger.performance_profile("data_processing"):
    # Your code here
    pass

Custom Metrics

# Track application-specific metrics
logger.add_custom_metric("active_users", 150)
logger.add_custom_metric("cache_hit_rate", 0.95)

# Track API calls
logger.track_api_call("/api/users", "GET", response_time=234, status_code=200)

Security Logging

logger.log_security_event("LOGIN_ATTEMPT", "MEDIUM", "Failed login attempt")

Batch Logging

logs = [
    {"level": "INFO", "message": "Processing item 1"},
    {"level": "INFO", "message": "Processing item 2"},
    ("SUCCESS", "Batch completed")
]
logger.batch_log(logs)

Contextual Logging

logger.log_with_context(
    "INFO",
    "User action performed",
    context={"user_id": 12345, "action": "file_upload"},
    tags=["user_activity", "audit"]
)

Reports and Dashboards

Generate Reports

# View insights in console
logger.view_insights(detailed=True)

# Create HTML dashboard
logger.view_insights(create_dashboard=True)

# Export data
logger.export_data("json", include_raw_data=True)
logger.export_data("csv")

Output Files

InsightLogger creates files in the .insight directory:

.insight/
├── app.log                           # Log file
├── insights_[session].db             # SQLite database
├── dashboard_[session].html          # HTML dashboard
├── log_frequency_[timestamp].png     # Charts
├── system_metrics_[timestamp].png
└── function_performance_[timestamp].png

Framework Integration

Flask Example

from flask import Flask
from insightlog import InsightLogger

app = Flask(__name__)
logger = InsightLogger("FlaskApp", enable_monitoring=True)

@app.route('/api/data')
@logger.log_function_time
def get_data():
    logger.track_api_call("/api/data", "GET", response_time=150, status_code=200)
    return {"data": "example"}

Documentation

For detailed documentation, examples, and advanced usage, see the docs directory.

License

MIT License. See LICENSE for details.

Contributing

Contributions are welcome! Please read our contributing guidelines and submit pull requests to our GitHub repository.

Support

• GitHub Issues: Report bugs
• Documentation: docs/
• PyPI: https://pypi.org/project/insightlog/