logxide 0.1.17

High-performance, Rust-powered drop-in replacement for Python's logging module

LogXide

High-Performance Rust-Powered Logging for Python

LogXide is a high-performance logging library for Python, delivering exceptional performance through its native Rust implementation. It provides a familiar logging API but prioritizes performance over full compatibility.

Key Features

• High Performance: Rust-powered logging with exceptional throughput
• Familiar API: Similar to Python's logging module (not a drop-in replacement)
• Thread-Safe: Complete support for multi-threaded applications
• Direct Processing: Efficient log message processing with native Rust handlers
• Rich Formatting: All Python logging format specifiers with advanced features
• Level Filtering: Hierarchical logger levels with inheritance
• Sentry Integration: Automatic error tracking with Sentry (optional)

⚠️ Important: Not a Drop-in Replacement

LogXide is NOT a drop-in replacement for Python's logging module. It prioritizes performance over compatibility:

Feature	Status	Notes
Basic logging API	✅	getLogger, info, debug, etc.
Formatters	✅	PercentStyle, StrFormatStyle, StringTemplateStyle
Rust handlers	✅	FileHandler, StreamHandler, RotatingFileHandler, HTTPHandler, OTLPHandler
Custom Python handlers	❌	Not supported - use Rust handlers only
Subclassing LogRecord	❌	Rust type, not subclassable
Subclassing Logger	❌	Rust type, not subclassable
pytest caplog	⚠️	Use caplog_logxide fixture
StringIO capture	❌	Use file-based logging

If your project requires:

• Subclassing LogRecord or Logger
• Custom Python handlers
• pytest caplog fixture (use caplog_logxide instead)
• Full stdlib logging compatibility

→ Use standard Python logging instead.

Quick Start

from logxide import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

logger = logging.getLogger('myapp')
logger.info('Hello from LogXide!')

LogXide automatically installs itself when imported.

Installation

# Basic installation
pip install logxide

# With Sentry integration
pip install logxide[sentry]

# Development dependencies
pip install logxide[dev]

📘 Usage Guide - Common mistakes, correct patterns, and troubleshooting

Documentation

• Usage Guide - Complete usage examples and API guide
• Integration Guide - Flask, Django, and FastAPI integration
• Sentry Integration - Automatic error tracking with Sentry
• Performance Benchmarks - Comprehensive performance analysis
• Architecture - Technical architecture and design
• Installation - Installation and setup guide
• Development - Contributing and development guide
• API Reference - Complete API documentation

Sentry Integration

LogXide includes optional Sentry integration for automatic error tracking:

# Configure Sentry first
import sentry_sdk
sentry_sdk.init(dsn="your-sentry-dsn")

# Import LogXide - Sentry integration is automatic!
from logxide import logging

logger = logging.getLogger(__name__)
logger.warning("This will appear in Sentry")
logger.error("This error will be tracked")

Features:

• Automatic detection of Sentry configuration
• Level filtering (WARNING and above sent to Sentry)
• Rich context including stack traces and custom data
• Zero configuration required

Performance

LogXide delivers exceptional performance through its Rust-powered native architecture. See our comprehensive benchmarks for detailed performance analysis.

Python 3.12 Benchmark Results (File I/O)

Real-world file logging performance (100,000 iterations):

Test Scenario	LogXide	Picologging	Python logging	vs Pico	vs Stdlib
Simple Logging	446,135 ops/sec	372,020 ops/sec	157,220 ops/sec	+20%	+184%
Structured Logging	412,235 ops/sec	357,193 ops/sec	153,547 ops/sec	+15%	+168%
Error Logging	426,294 ops/sec	361,053 ops/sec	155,332 ops/sec	+18%	+174%

Key highlights:

• 15-20% faster than Picologging (C-based) in production file I/O scenarios
• 2.7x faster than standard Python logging - upgrade with zero code changes!
• 2.5x faster than Structlog across all tests
• Native Rust I/O provides measurable performance advantages
• Consistent performance across all logging patterns

Limitations

LogXide uses Rust-native handlers only for maximum performance:

• Rust handlers only: FileHandler, StreamHandler, RotatingFileHandler, HTTPHandler, OTLPHandler
• No custom Python handlers: logger.addHandler() rejects Python logging.Handler subclasses
• No subclassing: LogRecord, Logger are Rust types (not subclassable)
• No StringIO capture: Use file-based logging for tests
• No pytest caplog: Use caplog_logxide fixture instead

Alternatives to LogRecord Subclassing

Instead of subclassing LogRecord, use these approaches:

Use Case	Alternative
Add custom fields	Use extra parameter: logger.info("msg", extra={"user_id": 123})
Add metadata to all logs	Use global_context in HTTPHandler
Transform log output	Use transform_callback in HTTPHandler
Dynamic context per batch	Use context_provider in HTTPHandler

Example - Adding custom fields:

from logxide import logging

logger = logging.getLogger('myapp')

# Use extra parameter (supports complex types: int, dict, list)
logger.info("User logged in", extra={
    "user_id": 12345,
    "ip": "192.168.1.1",
    "metadata": {"browser": "Chrome", "version": 120}
})

Example - Global context for all logs:

from logxide import HTTPHandler

handler = HTTPHandler(
    url="https://logs.example.com",
    global_context={
        "application": "myapp",
        "environment": "production",
        "version": "1.2.3"
    }
)

Example - Custom JSON transformation:

handler = HTTPHandler(
    url="https://logs.example.com",
    transform_callback=lambda records: {
        "logs": [{"msg": r["msg"], "level": r["levelname"]} for r in records],
        "meta": {"count": len(records)}
    }
)

Example - OpenTelemetry OTLP (Protobuf):

from logxide import OTLPHandler

handler = OTLPHandler(
    url="http://localhost:4318/v1/logs",
    service_name="my-service"
)

Compatibility

• Python: 3.12+ (3.14 supported)
• Platforms: macOS, Linux, Windows
• Dependencies: None (Rust compiled into native extension)

Third-party Library Compatibility

Library	Compatible	Notes
Flask	✅	Works with app.logger
Django	✅	Works with Django logging
FastAPI	✅	Works with Uvicorn
pytest	⚠️	Use caplog_logxide fixture instead of caplog
Sentry	✅	Auto-integration supported
structlog	❌	Requires custom handlers
infra_basement	❌	Requires LogRecord subclassing

Contributing

We welcome contributions! See our development guide for details.

# Quick development setup
git clone https://github.com/Indosaram/logxide
cd logxide
pip install maturin
maturin develop
pytest tests/

License

[Add your license information here]

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LogXide delivers high performance for applications that don't need full logging compatibility.

Built with Rust for high-performance Python applications.