Lumberjack¶
Lumberjack is a library which connects the Python logging framework to an Elasticsearch backend.
It has a number of useful features, including:
- Asynchronous logging by default
- Automatic time-rotating indices
- Elasticsearch mapping management
Quickstart¶
To use Lumberjack, you should have an Elasticsearch cluster already running.
You should instantiate the main Lumberjack class once in your application, specifying a list of Elasticsearch nodes to connect to:
from lumberjack import Lumberjack
lj = Lumberjack(hosts=[{'host': 'localhost', 'port': 9200}])
for an Elasticsearch node running locally on port 9200.
Once you have your Lumberjack object, you can then create log handlers to
attach to loggers from logging:
from logging import getLogger, INFO
a_logger = getLogger(__name__)
a_logger.setLevel(INFO)
handler = lj.get_handler()
a_logger.addHandler(handler)
Then, you can log events to a_logger (or any of its children in the
logger hierarchy) and they will be stored in Elasticsearch:
a_logger.error('Oh no! Something bad happened...')
Isn’t that easy!
Note: you might need to wait for the periodic flush of the log queue before the entry actually appears in Elasticsearch. By default this is every 30 seconds.