When you pass –log-level to Gunicorn, that is going to (unsurprisingly) be the log level for its appropriate handler. The last line of that snippet is significant. The key thing here (line #3) is to set the handlers of our Flask application logger to the Gunicorn logger (using the same output handlers and giving us a consistent logging experience). Then the next line (line #2 in the above snippet) we get a Logger object to the gunicorn.error logger. And the “not” would mean running my Python Flask application through Gunicorn in my workflow. By testing if name is equal to “ main”, that’s a good wayto see if it’s being run directly, or not. If _name_ != '_main_' : gunicorn_logger = logging. We need to wire up our Flask application to use those handlers so that all of our output, web application and WSGI, goes to the same place: The important part here, though, is that Gunicorn has its own loggers and handlers. This is where Gunicorn comes into the picture. Enter GunicornĪlthough Flask’s built-in WSGI is sufficient for development, it’s definitely not going to cut it in production. What we’re seeing above is Werkzeug (a WSGI utility library for Python, which Flask uses out-of-the-box) output. Running this application with python app.py (provided you named the above module the same), and calling curl localhost:8000 from another process, the output of this Flask application should look similar to the following: critical ( 'this is a CRITICAL message' ) return jsonify ( 'hello world' ) if _name_ = '_main_' : app. error ( 'this is an ERROR message' ) app. warning ( 'this is a WARNING message' ) app. route ( '/' ) def default_route (): '''Default route''' app. Python Logging has a default Logger – BasicConfig which we can use to log our messages.Import logging from flask import Flask, jsonify app = Flask ( _name_ ) app. Using the Default Logging System for Flaskįlask uses the Python logging system itself to trace out events during the application’s run-time. Using Filters, You can filter the log records based on some parameters other than the log-level. Hence you can mention the format of the Log being stored. Log Formatters are used to specify the layout of the log record. Using different handlers, you can route events to different files or even direct some to a file and the rest to a different location using multiple handlers. Handlers direct events/records to their destination. The logger then passes them to the handlers 2. The logger records only those logs whose severity is higher than it’s log level. The logger is configured to have a log-level. These events, recorded by the Logger, are called log records.Įach record has a security level. It provides the primary interface that logs events from your application. The whole logging procedure consists of 4 parts: Logger, handler, Formatter, and Filter. With the log of events, we can track the exact step where the error occurred-thus saving a lot of time for the developers. It also provides developers a separate mechanism to track and store all the events happening with your application in real-time it can track which IP address is accessing the site, which library is accessed, etc.ĭevelopers can later use this data to analyze the application’s performance and take necessary actions like scaling out.Īnother critical reason for logging is to debug errors. Hence, it makes the whole code running process more transparent. It helps you understand the flow of your program at a much deeper level. So what are you waiting for? Let’s get started !! Why Logging? Hello Friends! In this tutorial, we will learn about Flask logging.

Flask blueprint logging