# Async Logger
Async Logger is a custom Python logging library designed to log messages asynchronously to both the console and Elasticsearch.
It is built on top of Python's `logging` module and integrates with Elasticsearch using the `AsyncElasticsearch` client.
This allows for non-blocking, asynchronous logging in distributed environments.
## Features
- Asynchronous logging to Elasticsearch.
- Buffered log messages with periodic flushing to avoid performance hits.
- Customizable logging levels (DEBUG, INFO, WARNING, ERROR, CRITICAL).
- Integrated console logging.
## Installation
Install the package using pip:
```bash
pip install async_elastic_logger
```
## Usage
Here’s an example of how to use `AsyncLogger` in your Python application.
### 1. Define your configuration class using `pydantic`
You'll need to use `pydantic` to define a settings class that contains your Elasticsearch configurations.
```python
from pydantic import BaseSettings
class ElasticLoggerConfig(BaseSettings):
elastic_url: str
elastic_username: str
elastic_password: str
elastic_log_level: str = "WARNING"
elastic_log_index_name: str = "logs"
```
### 2. Use the `get_logger` function to get the singleton logger
Once you have the configuration, you can get the logger instance using the `get_logger` function. The logger is created as a singleton, so the same instance will be returned every time you call `get_logger`.
```python
from async_logger.logger import get_logger, ElasticLoggerConfig
# Define your Elasticsearch logger configurations
config = ElasticLoggerConfig(
elastic_url="https://your-elasticsearch-url",
elastic_username="your-username",
elastic_password="your-password",
elastic_log_level="INFO", # or DEBUG, ERROR, etc.
elastic_log_index_name="your-log-index"
)
# Get the singleton logger instance
logger = get_logger(config)
# Log messages
await logger.info("This is an info message")
await logger.error("This is an error message")
await logger.debug("This is a debug message")
```
### 3. Handling log levels
You can specify the logging level for Elasticsearch logs using the `elastic_log_level` field in your configuration. It accepts values like `DEBUG`, `INFO`, `WARNING`, `ERROR`, or `CRITICAL`.
For example:
```python
config = ElasticLoggerConfig(
elastic_url="https://your-elasticsearch-url",
elastic_username="your-username",
elastic_password="your-password",
elastic_log_level="DEBUG", # Logs at DEBUG level
elastic_log_index_name="your-log-index"
)
```
### 4. Configuration flexibility
You can easily switch the log level or Elasticsearch index by updating the configuration parameters. This allows you to fine-tune logging based on the environment or the level of detail you want in your logs.
### 5. Singleton behavior
Once the logger is instantiated with the configuration, subsequent calls to `get_logger` will return the same instance, ensuring that logging across your application is handled consistently.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
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