kakashi 0.2.1

High-performance logging utility for Python applications with advanced features

Kakashi - Professional High-Performance Logging Library

A modern, high-performance logging library designed for production applications that require both high throughput and excellent concurrency scaling.

🚀 Features

• High Performance: 60,000+ logs/sec throughput with balanced concurrency
• Thread-Safe: Minimal contention with thread-local optimizations
• Structured Logging: Field-based logging with minimal overhead
• Memory Efficient: <0.02MB memory usage for async operations
• Professional Code: Clean, maintainable architecture
• Drop-in Replacement: Compatible with Python's built-in logging

📊 Performance Targets

Metric	Target	Status
Throughput	60,000+ logs/sec	✅ EXCEEDED (66,116 logs/sec)
Concurrency Scaling	0.65x+	✅ EXCEEDED (1.17x scaling)
Memory Usage	<0.02MB	✅ Maintained
Structured Overhead	<10%	✅ Maintained

🏆 Benchmark Results

⚠️ LEGAL DISCLAIMER: The following benchmark results are provided for informational purposes only. Performance may vary based on system configuration, workload, and other factors. These results are not guarantees of performance and should not be used for commercial claims or comparisons without independent verification. Kakashi makes no warranties regarding performance characteristics.

Performance Comparison vs Industry Standards

Library	Throughput (logs/sec)	Concurrency Scaling	Async Throughput	Notes
Kakashi (Current)	56,310	1.17x	169,074	SUPERIOR performance
Standard Library	18,159	0.59x	N/A	Python built-in
Structlog	12,181	0.47x	N/A	Production ready
Loguru	14,690	0.46x	N/A	Feature rich

Performance Analysis

• Single-threaded Performance: Kakashi achieves 3.1x better throughput than standard library
• Concurrency Scaling: 1.17x scaling - adding threads improves performance (industry-leading)
• Async Performance: 169K logs/sec - 9.3x faster than standard library
• Memory Efficiency: Maintains low memory footprint across all scenarios

Note: These benchmarks were run on a development system and may not reflect production performance. Always test in your specific environment.

🏗️ Architecture

kakashi/
├── core/                    # Core logging implementation
│   ├── logger.py           # Main Logger and AsyncLogger classes
│   ├── records.py          # LogRecord, LogContext, LogLevel
│   ├── config.py           # Configuration system
│   ├── pipeline.py         # Pipeline processing components
│   ├── async_backend.py    # Asynchronous I/O backend
│   ├── structured_logger.py # Structured logging support
│   └── sinks.py            # Output destination system
├── performance_tests/       # Performance validation
│   └── validate_performance.py
└── README.md               # This file

📖 Quick Start

Basic Usage

from kakashi import get_logger, get_async_logger

# Synchronous logging
logger = get_logger(__name__)
logger.info("Application started", version="1.0.0")

# Asynchronous logging for high throughput
async_logger = get_async_logger(__name__)
async_logger.info("High-volume logging")

# Structured logging with fields
logger.info("User action", user_id=123, action="login", ip="192.168.1.1")

Advanced Configuration

from kakashi import setup_environment, production_config

# Production setup
config = production_config(
    service_name="my-api",
    version="2.1.0",
    enable_async_io=True
)
setup_environment(config)

Framework Integration

# FastAPI
from fastapi import FastAPI
from kakashi import setup_logging

app = FastAPI()
setup_logging("production", service_name="fastapi-app")

# Flask
from flask import Flask
from kakashi import setup_logging

app = Flask(__name__)
setup_logging("production", service_name="flask-app")

🔧 Installation

pip install kakashi

🧪 Performance Validation

Run the performance validation to ensure your installation meets production targets:

cd performance_tests
python validate_performance.py

This will test:

• Throughput performance (60K+ logs/sec)
• Concurrency scaling (0.65x+)
• Memory efficiency (<0.02MB)
• Structured logging overhead (<10%)

📚 API Reference

Core Classes

• Logger: High-performance synchronous logger
• AsyncLogger: Asynchronous logger with batch processing
• LogFormatter: Optimized message formatting

Main Functions

• get_logger(name, min_level=20): Get a synchronous logger
• get_async_logger(name, min_level=20): Get an asynchronous logger
• clear_logger_cache(): Clear logger cache

Configuration

• setup_environment(env, **kwargs): Configure logging environment
• production_config(**kwargs): Production-optimized configuration
• development_config(**kwargs): Development-optimized configuration

🎯 Use Cases

High-Throughput Applications

• API Services: Handle thousands of requests per second
• Data Processing: Log millions of events efficiently
• Real-time Systems: Minimal latency logging

Production Environments

• Microservices: Structured logging with context
• Distributed Systems: Async logging for scalability
• Cloud-Native Apps: Memory-efficient operation

🔍 Performance Characteristics

Throughput Optimization

• Thread-local buffer management
• Pre-computed level checks
• Direct I/O operations
• Minimal object allocation

Concurrency Optimization

• Lock-free hot paths
• Thread-local caching
• Batch processing
• Cache-line optimization

Memory Optimization

• Buffer pooling and reuse
• Zero-copy operations where possible
• Adaptive buffer sizing
• Reference counting for lifecycle management

🚨 Migration from v0.1.x

The v0.2.0 release maintains backward compatibility while providing significant performance improvements:

# Old v0.1.x code (still works)
from kakashi import setup, get_logger
setup("production")
logger = get_logger(__name__)

# New v0.2.0 code (recommended)
from kakashi import get_logger
logger = get_logger(__name__)  # Auto-configuration

🧭 Roadmap & Collaboration

We are looking for collaborators to help build the next evolution of Kakashi:

• Cloud log dump and long-term storage integrations (S3/GCS/Azure Blob, Kinesis, Kafka)
• Scalable log analysis pipelines (batch + streaming) with enrichment and alerting
• Incident reporting (SLOs/SLIs, error budgets, paging hooks, RCA helpers)
• First-class observability dashboards (Grafana/Loki, Kibana, Datadog, custom UI)

If you’re interested in shaping these capabilities, please:

• Open a discussion with your proposal and interests
• File an issue with [area:roadmap] label
• Or reach out via GitHub to coordinate design/ownership

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🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

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

⚖️ Legal Disclaimers

Performance Claims

• All performance metrics and benchmark results are provided for informational purposes only
• Performance may vary significantly based on system configuration, workload patterns, and environmental factors
• These results are not guarantees of performance and should not be used for commercial claims without independent verification
• Kakashi makes no warranties regarding performance characteristics or suitability for specific use cases

Benchmark Results

• Benchmark results are based on specific test conditions and may not reflect real-world performance
• Comparisons with other libraries are provided for context only and should not be considered definitive
• Users are encouraged to conduct their own performance testing in their specific environments
• Results may vary between different Python versions, operating systems, and hardware configurations

Usage and Liability

• Kakashi is provided "as is" without warranty of any kind
• Users assume all risk associated with the use of this software
• The authors and contributors are not liable for any damages arising from the use of Kakashi
• Always test thoroughly in your specific environment before production deployment

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Kakashi v0.2.0 - Professional High-Performance Logging for Python