kode-kronical 0.3.3

A lightweight Python performance tracking library with automatic data collection and visualization

Kode Kronical

High-performance Python library for automated performance monitoring and system metrics collection.

Overview

Kode Kronical provides automated performance monitoring for Python applications with:

• Function Performance Tracking: Automatic timing and profiling of Python functions
• System Metrics Collection: Real-time CPU, memory, and process monitoring
• Enhanced Exception Handling: Detailed error context with system correlation
• AWS Integration: Automatic DynamoDB uploads with 30-day TTL
• Web Dashboard: Comprehensive visualization via kode-kronical-viewer
• Zero Configuration: Works out-of-the-box with sensible defaults

Installation

pip install kode-kronical

Quick Start

1. Basic Usage

from kode_kronical import KodeKronical
import time

# Initialize the performance tracker
kode = KodeKronical()

# Use as decorator
@kode.time_it
def slow_function(n):
    time.sleep(0.1)
    return sum(range(n))

@kode.time_it(store_args=True)
def process_data(data, multiplier=2):
    return [x * multiplier for x in data]

# Call your functions - performance data is automatically collected
result1 = slow_function(1000)
result2 = process_data([1, 2, 3, 4, 5])

2. Configuration (Optional)

Create a .kode-kronical.yaml file in your project directory:

kode_kronical:
  enabled: true
  min_execution_time: 0.001

local:
  enabled: true
  data_dir: "./perf_data"

filters:
  exclude_modules:
    - "requests"
    - "boto3"

3. View Results

Automatic Data Collection:

• Local Mode: Performance data saved to ./perf_data/ as JSON files
• AWS Mode: Data uploaded to DynamoDB on program exit

Web Dashboard: Use the kode-kronical-viewer for visualization:

• Performance overview and metrics
• Function-by-function analysis
• Historical trends and comparisons
• System correlation analysis

Key Features

Enhanced Exception Handling

Kode Kronical captures detailed error context including system state when exceptions occur. See Exception Handling Guide for examples and configuration.

System Monitoring Daemon

Background daemon for continuous system monitoring that correlates with application performance. See Daemon Guide for complete setup and troubleshooting.

AWS Integration

Automatic upload to DynamoDB with optimized schema:

• 1-minute data intervals for production efficiency
• 30-day TTL for automatic cleanup
• Real-time dashboard updates

API Summary

from kode_kronical import KodeKronical

kode = KodeKronical()

# Decorator usage
@kode.time_it                           # Basic timing
@kode.time_it(store_args=True)         # Store function arguments
@kode.time_it(tags=["critical"])       # Add custom tags

# Programmatic access
summary = kode.get_summary()            # Get performance summary
results = kode.get_timing_results()     # Get detailed results
config = kode.get_config_info()         # Get configuration info

REST API

When using with kode-kronical-viewer, these endpoints are available:

• GET /api/performance/ - Performance data with filtering
• GET /api/hostnames/ - Available hostnames
• GET /api/functions/ - Function analysis data

Documentation

• Daemon Guide - Complete system monitoring setup and troubleshooting
• Exception Handling Guide - Enhanced error context and debugging
• AWS Setup Guide - DynamoDB configuration and deployment

License

MIT License - see LICENSE file for details.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests: pytest tests/
5. Submit a pull request

Support

• GitHub Issues
• Documentation