tbint-logger 0.4.5

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 tb-logger, set the following environment variables:

• LOG_LEVEL: The log level threshold (default: error). Possible values: debug, info, warn, error, critical.
• LOG_ENVIRONMENT: The logging environment (default: development, possible values: production, staging, development, local).

Example .env file:

LOG_LEVEL=debug
LOG_ENVIRONMENT=local

If you want to enable Datadog logs, you must also set DATADOG_API_KEY. Optionally, you can set DATADOG_API_ENDPOINT to use a different Datadog site and DATADOG_TAGS to add tags to your logs.

If you want the service name in Datadog to be different from the default that's passed to the logger constructor, you can set DATADOG_SERVICE_NAME to the desired value.

Example .env file:

LOG_LEVEL=debug
LOG_ENVIRONMENT=local
DATADOG_SERVICE_NAME=my-service
DATADOG_SOURCE=production
DATADOG_TAGS=env:production,team:backend
DATADOG_API_ENDPOINT=https://http-intake.logs.datadoghq.eu/api/v2/logs
DATADOG_API_KEY=your-datadog-api-key

Basic usage

LoggerData class

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

Attribute	Type	Description
service	Optional[str]	The service generating the log.
system	Optional[str]	The system generating the log.
project	Optional[str]	The project name.
component	Optional[str]	The system component generating the log.
class_name	Optional[str]	The class name where the log originates.
obfuscate_context_fields	List[str]	List of fields to obfuscate in the context.
obfuscate_context_character	Optional[str]	Character to use for obfuscation.

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)
  

• Critical: Use for critical issues that need immediate attention.

  logger.critical_sync(data)
  await logger.critical(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.
project	str	The project name (optional).
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

tb-logger is licensed under the MIT License. See the LICENSE file for details.

Development

python3 -m venv venv
source venv/bin/activate
rm -rf dist/*
python3 -m pip install -r requirements.txt
python3 -m build
python3 -m twine upload --repository pypi dist/*

Update Requirements

python3 -m pip freeze > requirements.txt