async-its-logger 0.1.0

Asynchronous logger for high performance applications that incorporates multiple observers, logs in batches, and includes automatic retries and fallback if services are not available.

Async ITS Logger

This logger is an easy-to-implement asynchronous logging library that uses the Observer Pattern to enable your application to log in multiple ways. It is designed to work in both asynchronous and synchronous environments. With ITS Logger you can log to several destinations such as Console, Local Files, AWS S3, MongoDB, Redis, and Datadog—all configurable via a YAML configuration file.

Features

• Asynchronous Logging: Efficient logging that runs in the background without blocking your application.
• Observer Pattern: Supports multiple logging destinations (observers) simultaneously.
• Flexible Configuration: Easily configure log batching, retry delays, and more using a simple YAML file.
• Dual Environment Support: Works with both asynchronous and synchronous applications.
• Extensible: Add or remove logging observers as needed.

Installation

From PyPI

Once ITS Logger is published on PyPI, install it with pip:

pip install its_logger

From Source

Clone the repository:

bash

git clone https://github.com/yourusername/its_logger.git
cd its_logger

Install using pip:

pip install .

Requirements

Python 3.7 or higher

Configuration

ITS Logger is configured using a YAML file (e.g., config.yaml). Below is an example configuration file with placeholder values. Replace the placeholders with your own configuration details.

logging:
  enable_console: false
  enable_local_file: false
  enable_datadog: true
  enable_s3: false
  enable_mongodb: false
  enable_redis: false

  batch_interval: 1           # Interval in seconds to send logs
  batch_size: 10              # Maximum number of logs to send in one batch
  retry_delay: 1              # Initial retry delay (in seconds)
  max_retries: 5              # Maximum number of retries
  max_wait_time: 10           # Maximum seconds logs can wait before being flushed

  log_level: "INFO"
  local_log_path: "./logs"

datadog:
  api_key: "<YOUR_DATADOG_API_KEY>"
  app_key: "<YOUR_DATADOG_APP_KEY>"
  endpoint: "https://http-intake.logs.<YOUR_REGION>.datadoghq.com"

aws:
  s3_bucket_name: "<YOUR_S3_BUCKET_NAME>"
  s3_prefix: "logs"

mongodb:
  uri: "mongodb://localhost:27017"
  database: "logging"
  collection: "logs"
  execution_times_collection: "execution_times"

redis:
  host: "localhost"
  port: 6379
  db: 0
  redis_list: "log_entries"
  execution_time_key: "execution_times"

Note:

• For Datadog, make sure you use the appropriate values for your account and region.
• You can enable or disable individual observers by setting their flags to true or false under the logging section.

Usage Example

Below is a comprehensive example demonstrating how to use ITS Logger in your application:

import time
import yaml
import asyncio
from its_logger.async_its_logger import AsyncITSLogger
from its_logger.observers.mongodb_observer import MongoDBObserver
from its_logger.observers.redis_observer import RedisObserver
from its_logger.observers.datadog_observer import DatadogObserver
from its_logger.observers.s3_observer import S3Observer
from its_logger.observers.local_file_observer import LocalFileObserver
from its_logger.observers.console_observer import ConsoleObserver

# Load configuration
with open("config.yaml", "r") as file:
    config = yaml.safe_load(file)

# Initialize logger (which creates its own event loop in a background thread)
logger = AsyncITSLogger()

# Register enabled observers dynamically based on the config
if config["logging"]["enable_datadog"]:
    print("Datadog observer enabled.")
    datadog_observer = DatadogObserver()
    logger.register_observer(datadog_observer)

if config["logging"]["enable_s3"]:
    print("S3 observer enabled.")
    s3_observer = S3Observer()
    logger.register_observer(s3_observer)

if config["logging"]["enable_local_file"]:
    print("Local file observer enabled.")
    local_file_observer = LocalFileObserver()
    logger.register_observer(local_file_observer)

if config["logging"]["enable_console"]:
    print("Console observer enabled.")
    console_observer = ConsoleObserver()
    logger.register_observer(console_observer)

if config["logging"]["enable_mongodb"]:
    print("MongoDB observer enabled.")
    mongodb_observer = MongoDBObserver()
    logger.register_observer(mongodb_observer)
    # Start auto-flush for MongoDB observer using the logger's loop
    asyncio.run_coroutine_threadsafe(mongodb_observer.auto_flush(), logger.loop)

if config["logging"]["enable_redis"]:
    print("Redis observer enabled.")
    redis_observer = RedisObserver()
    logger.register_observer(redis_observer)
    # Start auto-flush for Redis observer using the logger's loop
    asyncio.run_coroutine_threadsafe(redis_observer.auto_flush(), logger.loop)

# Example usage: simulate a request processing function
def process_request():
    task_id = logger.generate_task_id()
    print(f"Generated Task ID: {task_id}")

    start_time = time.time()
    logger.log("MyApp", "RequestHandler", task_id=task_id, message="Request started.")

    try:
        for i in range(3):
            logger.log("MyApp", f"ProcessingStep{i+1}", task_id=task_id, message=f"Processing step {i+1}...")
            time.sleep(1)  # Simulate processing time
        raise Exception("Something went wrong!")
    except Exception as e:
        logger.log(
            "MyApp",
            "ErrorHandler",
            task_id=task_id,
            status="ERROR",
            message="An error occurred while processing the request",
            exception=e
        )

    end_time = time.time()
    execution_time = round(end_time - start_time, 2)
    logger.log(
        "MyApp",
        "RequestHandler",
        task_id=task_id,
        status="INFO",
        message=f"Request completed in {execution_time} seconds."
    )

# Run simulated requests
for _ in range(1):
    process_request()
    time.sleep(2)

print("Main application finished.")

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Make your changes and commit them with clear messages.
4. Submit a pull request describing your changes.

License

This project is licensed under the MIT License.

Contact

For questions or support, please contact [e-mail]