1/1/2024 0 Comments Flask blueprint logger![]() Now when we run gunicorn -workers=2 -bind=0.0.0. 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. Import logging from flask import Flask, jsonify app = Flask ( _name_ ) if _name_ != '_main_' : gunicorn_logger = logging. The full sample code of this example is as follows: Now when we set -log-level=warning when invoking Gunicorn, this same logging level is used for Flask’s logger. By letting that trickle down to the Flask application logger’s logging level, we now have a single source of truth for log levels: The Gunicorn logging level. 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. from flask import Flask: from werkzeug.utils import findmodules, importstring: def configurelogging(): register root logging: logging.basicConfig(levellogging.DEBUG) logging.getLogger('werkzeug').setLevel(logging.INFO) def registerblueprints(app): '''Automagically register all blueprint packages: Just take a look in the blueprints. ![]() 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. Flask is no different, the default loggers give you minute detail about what Werkzeug is doing but inserting your actual application logs into the stream is a chore, and when you have internal. apiflask.validators: The marshmallow validators.Import logging from flask import Flask, jsonify app = Flask ( _name_ ) app.Beware that the aliases ( Url, Str, Int, Bool, etc.) were removed. apiflask.fields: The marshmallow fields, contain the fields from both marshmallow and Flask-Marshmallow.Notice it sets the set_class to OrderedSet by default. apiflask.Schema: The base marshmallow schema class.You can build marshmallow schemas just like before, but APIFlask also exposes some marshmallow APIs for convenience: Relationship with marshmallow ¶ĪPIFlask accepts marshmallow schema as data schema, uses webargs to validate the request data against the schema, and uses apispec to generate the OpenAPI representation from the schema. ![]() Other than that, you are actually using Flask. In a word, to make Web API development in Flask more easily, APIFlask provides APIFlask and APIBlueprint to extend Flask's Flask and Blueprint objects and it also ships with some helpful utilities. From apiflask import APIFlask, Schema, abort from apiflask.fields import Integer, String from apiflask.validators import Length, OneOf app = APIFlask ( _name_ ) pets = [ '
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