kode-kronical 0.2.7

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
• 1-minute Data Intervals: Optimized for production with efficient 1-minute sampling
• 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

Quick Start

1. Create Configuration File

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"
  format: "json"

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

2. Use in Your Code

from kode_kronical import KodeKronical
import time

# Initialize the performance tracker (loads .kode-kronical.yaml automatically)
perf = KodeKronical()

# Method 1: Use as decorator
@perf.time_it
def slow_function(n):
    time.sleep(0.1)
    return sum(range(n))

# Method 2: Use as decorator with arguments
@perf.time_it(store_args=True)
def process_data(data, multiplier=2):
    return [x * multiplier for x in data]

# Call your functions
result1 = slow_function(1000)
result2 = process_data([1, 2, 3, 4, 5])

# Performance data is automatically collected and uploaded
# - Local mode: Saved to ./perf_data/ as JSON files
# - AWS mode: Uploaded to DynamoDB on program exit
# - View data using the web dashboard at http://localhost:8000

# Optional: Get timing results programmatically
summary = perf.get_summary()
print(f"Tracked {summary['call_count']} function calls")

3. View Results

Automatic Data Collection:

• Local Mode: Performance data is automatically saved to ./perf_data/ as JSON files
• AWS Mode: Data is automatically uploaded to DynamoDB when your program exits

Web Dashboard: For visualizing performance data, use the separate kode-kronical-viewer Django dashboard that provides:

• Performance overview and metrics
• Function-by-function analysis
• Historical trends and comparisons
• Advanced filtering and search

For AWS integration and production setup, see the Configuration section below.

Building and Publishing

This project includes automated scripts for building and publishing the package with automatic version incrementing.

Quick Build & Upload

# Build and upload in one step (recommended)
./upload_package.sh

This script will:

• Automatically increment the version (0.1.4 → 0.1.5)
• Build the package
• Validate the package
• Give you options to upload to Test PyPI or Production PyPI
• Use your configured API tokens from .pypirc

Build Only

# Just build the package (increments version)
python3 build_package.py

This script will:

• Automatically increment the version
• Clean previous builds
• Build both wheel and source distribution
• Validate the package

Manual Build (Advanced)

# Traditional manual build (no version increment)
python -m build

# Manual upload to PyPI
pip install twine
twine upload dist/*

Version Management

• Automatic: Both scripts automatically increment version by 0.01 each run
• Current version: Check with python3 -c "from version_manager import get_current_version; print(get_current_version())"
• Manual increment: Run python3 version_manager.py

PyPI Configuration

Create a .pypirc file in the project root with your API tokens:

[distutils]
index-servers =
    pypi
    testpypi

[pypi]
username = __token__
password = your-pypi-api-token

[testpypi]
repository = https://test.pypi.org/legacy/
username = __token__
password = your-testpypi-api-token

Testing Your Package

Test on Test PyPI first (recommended):

1. Run ./upload_package.sh and choose option 1 (Test PyPI)
2. Install and test: pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ kode-kronical
3. If everything works, upload to production PyPI

Kode Kronical

This library is used to track and represent the performance of Python code that is executed via an easy to install and configure Python library.

Installation

pip install kode-kronical

Development

Setup Development Environment

# Clone the repository
git clone https://github.com/jeremycharlesgillespie/kode-kronical.git
cd kode-kronical

# Create virtual environment
python3 -m venv venv

# Activate virtual environment
source venv/bin/activate  # On macOS/Linux
# or
venv\Scripts\activate     # On Windows

# Install dependencies
pip install -r requirements.txt

# For development (includes testing tools)
pip install -r requirements-dev.txt

# Install pre-commit hooks (optional)
pre-commit install

Configuration

KodeKronical uses YAML configuration files for flexible and easy setup. Configuration sources are loaded in priority order:

1. Default configuration (built-in defaults)
2. User configuration files (.kode-kronical.yaml, kode-kronical.yaml)
3. Runtime overrides (passed to KodeKronical constructor)

Quick Start - Local Mode (No AWS Required)

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

kode_kronical:
  enabled: true
  debug: false
  min_execution_time: 0.001

local:
  enabled: true  # Use local storage, no AWS required
  data_dir: "./perf_data"
  format: "json"
  max_records: 1000

filters:
  exclude_modules:
    - "boto3"
    - "requests"
    - "urllib3"
  track_arguments: false

AWS DynamoDB Mode

For production AWS usage:

kode_kronical:
  enabled: true
  min_execution_time: 0.001

aws:
  region: "us-east-1"
  table_name: "kode-kronical-data"
  auto_create_table: true

upload:
  strategy: "on_exit"  # on_exit, real_time, batch, manual

local:
  enabled: false  # Disable local storage

Advanced Configuration

See .kode-kronical.yaml.example for all configuration options including:

• Performance filtering (modules, functions, execution time thresholds)
• Upload strategies (real-time, batch, manual)
• Logging configuration
• Dashboard settings

Runtime Configuration

You can also configure KodeKronical programmatically:

from kode_kronical import KodeKronical

# Local-only mode
perf = KodeKronical({
    "local": {"enabled": True},
    "kode_kronical": {"debug": True}
})

# AWS mode with custom settings
perf = KodeKronical({
    "aws": {
        "region": "us-east-1",
        "table_name": "my-perf-data"
    },
    "local": {"enabled": False}
})

Configuration File Locations

KodeKronical searches for configuration files in this order:

1. ./kode-kronical.yaml (current directory)
2. ./.kode-kronical.yaml (current directory, hidden file)
3. ~/.kode-kronical.yaml (home directory)
4. ~/.config/kode-kronical/config.yaml (XDG config directory)

AWS Setup

For AWS mode:

1. Configure AWS CLI: aws configure
2. Create your .kode-kronical.yaml with AWS settings
3. KodeKronical will automatically create DynamoDB tables if needed

See AWS_SETUP.md for detailed AWS configuration instructions.

Virtual Environment Usage

Always activate the virtual environment before running KodeKronical:

# Activate virtual environment
source venv/bin/activate

# Run your KodeKronical application
python3 tester.py

# Deactivate when done
deactivate

Web Dashboard

For a comprehensive web dashboard to visualize and analyze your kode-kronical performance data, use the separate kode-kronical-viewer project.

kode-kronical-viewer Features

• Performance Overview: Key metrics, slowest functions, most active hosts
• Advanced Filtering: Filter by hostname, date range, function name, session ID
• Sorting: Sort records by timestamp, hostname, total calls, wall time, etc.
• Function Analysis: Detailed performance analysis for specific functions
• REST API: Programmatic access to performance data
• Real-time Data: Automatically displays latest performance data from DynamoDB

Installation

# Install the dashboard separately
pip install kode-kronical-viewer

# Or clone and run the standalone project
git clone https://github.com/jeremycharlesgillespie/kode-kronical-viewer
cd kode-kronical-viewer
pip install -r requirements.txt
python start_viewer.py

Visit the kode-kronical-viewer repository for detailed setup and usage instructions.

Dashboard Screenshots

Here's what you can expect when using the kode-kronical-viewer web dashboard to visualize your kode-kronical performance data:

Main Dashboard Overview

Complete performance overview with key metrics, slowest functions, and recent activity

Performance Records Browser

Browse and filter all performance records with advanced search capabilities

Detailed Record Analysis

Detailed breakdown of individual performance records showing function-level metrics

Function Performance Analysis

Cross-record analysis of specific functions with performance trends over time

REST API Endpoints

The dashboard also provides JSON API endpoints for programmatic access:

Performance Metrics API

GET /api/metrics/ - Summary performance metrics in JSON format

{
    "total_records": 2,
    "total_sessions": 2,
    "unique_hostnames": ["Mac.home.local"],
    "unique_functions": [
        "slow_io_operation",
        "cpu_intensive_task", 
        "check_aws_credentials",
        "mixed_workload",
        "fast_calculation",
        "variable_duration"
    ],
    "avg_session_duration": 0.0,
    "slowest_functions": [
        ["check_aws_credentials", 0.294],
        ["slow_io_operation", 0.105],
        ["mixed_workload", 0.055]
    ],
    "most_active_hosts": [
        ["Mac.home.local", 14]
    ]
}

Hostnames API

GET /api/hostnames/ - List of unique hostnames for filtering

{
    "hostnames": ["Mac.home.local"]
}

Functions API

GET /api/functions/ - List of unique function names for analysis

{
    "functions": [
        "check_aws_credentials",
        "cpu_intensive_task",
        "fast_calculation",
        "mixed_workload", 
        "slow_io_operation",
        "variable_duration"
    ]
}

System Monitoring Daemon

kode-kronical includes a background daemon for continuous system monitoring:

Installation

# Install the daemon
sudo cp kode-kronical-daemon /usr/local/bin/
sudo chmod +x /usr/local/bin/kode-kronical-daemon

# Create configuration directory
mkdir -p ~/.config/kode-kronical/
cp config/daemon.yaml.example ~/.config/kode-kronical/daemon.yaml

# Edit configuration (set AWS region, table name, etc.)
nano ~/.config/kode-kronical/daemon.yaml

Configuration

The daemon configuration supports:

daemon:
  sample_interval: 60.0  # Collect data every minute
  max_samples: 1        # Upload immediately (no batching)
  enable_dynamodb_upload: true
  dynamodb_table_name: kode-kronical-system-v2  # Optimized table structure
  dynamodb_region: us-east-1

monitoring:
  auto_track_python: true
  cpu_alert_threshold: 90
  memory_alert_threshold: 85

Running the Daemon

# Start daemon (foreground)
kode-kronical-daemon -c ~/.config/kode-kronical/daemon.yaml start

# Check status
kode-kronical-daemon -c ~/.config/kode-kronical/daemon.yaml status

# Stop daemon  
kode-kronical-daemon -c ~/.config/kode-kronical/daemon.yaml stop

Optimized Data Collection

The system now uses:

• 1-minute intervals: Efficient sampling every 60 seconds
• Optimized storage: Frontend-ready format with hour-based partitioning
• Automatic TTL: Data expires after 30 days
• Real-time access: Sub-minute latency for dashboard updates

Code Formatting

# Activate virtual environment
source venv/bin/activate

# Format code (if dev dependencies installed)
black src tests
isort src tests
flake8 src tests
mypy src

License

MIT License - see LICENSE file for details.