loralogger 0.2.1

Custom logging handler for AskLora projects

LORA Logger

This package contains both the customised handler for saving the logs into a elasticsearch database, and a factory for creating customised loggers that can use that handler.

The customised handler will forward the logs to an existing logging service through our own celery service. This logging service will handle the logs and input them into the database. This is done to unionise the logs into a single database.

Diagram

flowchart LR

    services["Services\n<small>All backend projects\nthat need the logging\nsystem</small>"]-->producer[[Producer]]
    subgraph "LoraLogger package"
    producer-->queue[Queue]
    end
    queue-->consumer[[Consumer]]
    subgraph "AskLora logger service"
    consumer-->database[(<small>ElasticSearch\nDatabase</small>)]
    end

How to use

Currently, this package exports a logging handler. Loggers with this handler will be automatically send the records to the elasticsearch server set using the environment variable.

Package installation

there are two ways to install this pacakge

• install the package locally. first, build the project:

  poetry build
  

  then you can install using pip

  pip install /path/to/logger/dist/loralogger-0.2.0-py3-none-any.whl
  

• Install the package from pip

  pip install loralogger
  

Using this package

First, set these environment variables:

BROKER_URL=<your-rabbitmq-endpoint>
RESULT_BACKEND=<your-redis-endpoint>

Then you can use the logger in two different ways:

1. Use the logger factory

   • import the logger factory

     from loralogger import LoraLogger
     

   • create a logger instance, the logger name should point to the Elasticsearch index name you want to send the logs into, with the word "-logs" appended to it (this, for instance, will send the logs to backend-logs index)

     test_logger = LoraLogger.get_logger('backend')
     

   • use the logger

     test_logger.warning("Careful!")
     

2. Use the handler directly to your own logger instance:

   • import the handler

     from loralogger import LogToESHandler
     

   • initialise logging instance

     backend_logger = logging.getLogger("backend")
     

   • Create the handler instance, same as the above, the label should point to an existing Elasticsearch index

     handler = LogToESHandler(label="backend")
     

   • add the handler instance to the logger

     backend_logger.addHandler(handler)
     

   • Use the logger

     backend_logger.info("This is an info")
     

Notes

• if the pip installation fails, check this link https://github.com/celery/librabbitmq/issues/131#issuecomment-661884151