dc-logger 1.2.0

A flexible logging library with support for cloud providers, multi-tenancy, and structured logging

DC Logger

A flexible, async-first structured logging library for Python with support for multiple handlers, cloud providers, distributed tracing, and multi-tenancy.

Features

• Async-first design - Non-blocking logging for high-performance applications
• Structured logging - JSON-based logs with entities, actions, and context
• Multiple handlers - Console, file, and cloud output simultaneously
• Cloud integrations - Datadog, AWS CloudWatch, GCP, Azure support
• Distributed tracing - Built-in correlation with trace/span IDs
• Decorator logging - Automatic function entry/exit logging with @log_call
• Multi-tenant support - Tenant context for SaaS applications
• Colored console output - ANSI colors for better visibility

Installation

pip install dc_logger

# With Datadog support
pip install dc_logger[datadog]

# With all extras
pip install dc_logger[all]

Quick Start

import asyncio
from dc_logger import DCLogger, ConsoleLogConfig, LogLevel

async def main():
    config = ConsoleLogConfig(level=LogLevel.INFO)
    logger = DCLogger(config, "my_app")
    
    await logger.info("Application started")
    await logger.warning("This is a warning")
    await logger.error("Something went wrong")
    
    await logger.close()

asyncio.run(main())

Using the Decorator

from dc_logger import log_call

@log_call(action_name="process", include_params=True)
async def process_data(data_id: str):
    # Automatically logs entry, exit, duration, and errors
    return {"status": "processed"}

Structured Logging with Context

from dc_logger import LogEntity

entity = LogEntity(type="dataset", id="ds_123", name="Sales Data")

await logger.info(
    "Dataset processed",
    entity=entity,
    action="process_dataset",
    duration_ms=1500,
    extra={"rows": 10000}
)

Multi-Handler Configuration

from dc_logger import create_console_file_config

config = create_console_file_config(
    file_path="logs/app.log",
    level=LogLevel.INFO,
    pretty_print=True
)
logger = DCLogger(config, "my_app")

Documentation

Document	Description
USAGE.md	Comprehensive usage guide
API_REFERENCE.md	Complete API documentation
docs/getting-started.md	Step-by-step introduction
docs/configuration.md	Configuration options
docs/best-practices.md	Recommended patterns
examples/	Working example scripts

For AI Agents

See .ai/context.md for a quick reference guide optimized for AI agents.

Key Components

Component	Import	Description
DCLogger	from dc_logger import DCLogger	Main logger class
ConsoleLogConfig	from dc_logger import ConsoleLogConfig	Console configuration
LogLevel	from dc_logger import LogLevel	Log levels (DEBUG, INFO, etc.)
LogEntity	from dc_logger import LogEntity	Entity context for logs
HTTPDetails	from dc_logger import HTTPDetails	HTTP request details
log_call	from dc_logger import log_call	Function logging decorator
get_logger	from dc_logger import get_logger	Get global logger
correlation_manager	from dc_logger import correlation_manager	Distributed tracing

Log Levels

LogLevel.DEBUG      # Detailed debugging information
LogLevel.INFO       # General informational messages
LogLevel.WARNING    # Warning messages for potential issues
LogLevel.ERROR      # Error messages for failures
LogLevel.CRITICAL   # Critical errors requiring immediate attention

Cloud Integrations

Datadog

from dc_logger import create_console_datadog_config

config = create_console_datadog_config(
    datadog_api_key="your-api-key",
    datadog_service="my-service",
    datadog_env="production"
)

Architecture

See documentation/architecture.md for design details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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

Author

Jae Wilson (@jaewilson07)