tbint-logger 0.0.15

A simple logger for tbint projects

TBint Logger Python

TBIntLogger is a Python-based logging library designed to simplify logging messages and data to Datadog.

It supports both synchronous and asynchronous logging, providing flexibility for various application needs.

Features

• Log messages at different levels: debug, info, warn, error.
• Support for both synchronous and asynchronous logging.
• Customizable through environment variables.
• Easy integration with Datadog for centralized logging and monitoring.

Installation

You can install tbint-logger from PyPI:

pip install tbint-logger

Getting Started

Environment Variables

Before using TBIntLogger, set the following environment variables:

• DD_SERVICE_NAME: The name of the service (default: unknown).
• DD_SOURCE: The source of the logs (default: unknown, usually this is production or development).
• DD_TAGS: Tags to associate with the logs (or empty).
• DD_API_ENDPOINT: The Datadog API endpoint for log ingestion.
• DD_APP_KEY: Your Datadog API key.
• LOG_LEVEL: The log level threshold (default: debug). Possible values: debug, info, warn, error.

Example .env file:

DD_SERVICE_NAME=my-service
DD_SOURCE=production
DD_TAGS=env:production,team:backend
DD_API_ENDPOINT=https://http-intake.logs.datadoghq.com/api/v2/logs
DD_APP_KEY=your-datadog-api-key
LOG_LEVEL=info

Load the environment variables using python-dotenv:

Basic Usage

Synchronous Logging

from tbint_logger import Logger, LoggerData

# Init with default values
logger = Logger(
    system="my-system",
    component="auth",
    class_name="AuthService",
    # NOTE:
    # This will obfuscate the context (list or dict) fields
    # recursively, with the character '*'.
    # Matches are case-insensitive.
    # INFO: This is completely optional.
    obfuscate_context_fields=["password", "email", "cc_number", "cvv"],
    obfuscate_context_character="*"
)

# Default values can be overridden
# on each call to the logger
data = LoggerData(
    system="my-system2",
    event="user-login",
    correlation_id="abc123",
    component="auth2",
    class_name="AuthService2",
    method="login",
    description="User successfully logged in",
    duration_ms=120,
    context={
        "user_id": 42,
        "email": "[e-mail]",
        "password": "secret",
        "cc_number": "1234567890",
        "cvv": "123"
    }
)

logger.info_sync(data)

Asynchronous Logging

import asyncio
from tbint_logger import Logger, LoggerData

# Init with default values
logger = Logger(
    system="my-system",
    component="auth",
    class_name="AuthService",
)

# Default values can be overridden
# on each call to the logger
data = LoggerData(
    system="my-system2",
    event="user-login",
    correlation_id="abc123",
    component="auth2",
    class_name="AuthService2",
    method="login",
    description="User successfully logged in",
    duration_ms=120,
    context={"user_id": 42}
)

async def log_event():
    await logger.info(data)

asyncio.run(log_event())

Logging Levels

• Debug: Use for detailed diagnostic information.

  logger.debug_sync(data)
  await logger.debug(data)
  

• Info: Use for general informational messages.

  logger.info_sync(data)
  await logger.info(data)
  

• Warn: Use for warnings that don't require immediate attention.

  logger.warn_sync(data)
  await logger.warn(data)
  

• Error: Use for errors that require attention.

  logger.error_sync(data)
  await logger.error(data)
  

LoggerData Class

The LoggerData class is used to structure log messages. It accepts the following attributes:

Attribute	Type	Description
system	str	The system generating the log.
event	str	The event type (e.g., "user-login").
correlation_id	str	A unique identifier for correlating logs.
component	str	The system component generating the log.
class_name	str	The class name where the log originates.
method	str	The method where the log originates.
description	str	A description of the log event.
duration_ms	int	Duration of the event in milliseconds.
context	dict	Additional context data to include in the log.

How It Works

1. Environment Configuration: Reads environment variables for Datadog configuration.
2. Log Message Construction: Formats log messages with metadata and timestamp.
3. Datadog Integration: Sends logs to Datadog via API.
4. Sync/Async Options: Offers both synchronous and asynchronous logging for flexible use cases.

License

TBIntLogger is licensed under the MIT License. See the LICENSE file for details.

Development

python3 -m venv venv
source venv/bin/activate
python3 -m pip install --upgrade build
python3 -m build
python3 -m pip install --upgrade twine
python3 -m twine upload --repository pypi dist/*