Say Goodbye to Print(): Use Logging Module for Efficient Debugging

Many people begin our programming journey with YouTube movies, and for the sake of simplicity, they typically use print() statements to trace bugs. That is honest sufficient, however as newbies undertake this behavior, it might turn out to be problematic. Though these statements would possibly work for easy scripts, as your codebase expands, this strategy turns into extremely inefficient. Subsequently, on this article, I’ll introduce you to Python’s built-in logging module, which solves this drawback. We are going to see what logging is, the way it differs from the print() statements, and we may even cowl a sensible instance to completely perceive its performance.

Why Use the Logging Module As an alternative of Print()?

After we speak about debugging, the Python logging module gives far more detailed info than easy print() statements. This consists of timestamps, module names, log ranges, and line numbers the place errors occurred, and many others. These additional particulars assist us perceive the habits of our code extra successfully. The data we need to log depends upon the wants of the applying and the developer’s desire. So, earlier than we proceed additional, let’s talk about log ranges and how one can set them.

Logging Ranges

 
You’ll be able to management the quantity of data you need to see utilizing these log ranges. Every log degree has a numerical worth that denotes its severity, with increased values indicating extra extreme occasions. For instance, if you happen to set your log degree to WARNING, you are telling the logging module to solely present you messages which can be of WARNING degree or increased. This implies you will not see any DEBUG, INFO, or different much less extreme messages. This manner, you may concentrate on the necessary occasions and ignore the noise

Right here’s a desk that exhibits the small print of what every log degree represents:

Setting Up the Logging Module

To make use of the logging module, it is advisable comply with some steps for configuration. This consists of making a logger, setting the logging degree, making a formatter, and defining a number of handlers. A handler mainly decides the place to ship your log messages, corresponding to to the console or a file. Let’s begin with a easy instance. We’ll arrange the logging module to do two issues: first, it’s going to present messages on the console, giving us helpful info (on the INFO degree). Second, it’s going to save extra detailed messages to a file (on the DEBUG degree). I might find it irresistible if you happen to might comply with alongside!

1. Setting the log degree

The default degree of the logger is ready to WARNING. In our case, our two handlers are set to DEBUG and INFO ranges. Therefore, to make sure all messages are managed correctly, now we have to set the logger’s degree to the bottom degree amongst all handlers, which, on this case, is DEBUG.

import logging

# Create a logger
logger = logging.getLogger(__name__)

# Set logger degree to DEBUG
logger.setLevel(logging.DEBUG)

2. Making a Formatter

You’ll be able to personalize your log messages utilizing formatters. These formatters resolve how your log messages will look. Right here, we are going to arrange the formatter to incorporate the timestamp, the log degree, and the message content material utilizing the command beneath:

formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')

3. Creating Handlers

As mentioned beforehand, handlers handle the place your log messages shall be despatched. We are going to create two handlers: a console handler to log messages to the console and a file handler to write down log messages to a file named ‘app.log’.

console_handler = logging.StreamHandler()
console_handler.setLevel(logging.INFO)
console_handler.setFormatter(formatter)

file_handler = logging.FileHandler('app.log')
file_handler.setLevel(logging.DEBUG)
file_handler.setFormatter(formatter)

Each handlers are then added to the logger utilizing the addHandler() technique.

logger.addHandler(console_handler)
logger.addHandler(file_handler)

4. Testing the Logging Setup

Now that our setup is full, let’s check if it is working accurately earlier than shifting to the real-life instance. We will log some messages as follows:

logger.debug('It is a debug message')
logger.information('That is an information message')
logger.warning('It is a warning message')
logger.error('That is an error message')
logger.vital('It is a vital message')

Whenever you run this code, it’s best to see the log messages printed to the console and written to a file named ‘app.log’, like this:

Console

2024-05-18 11:51:44,187 - INFO - That is an information message
2024-05-18 11:51:44,187 - WARNING - It is a warning message
2024-05-18 11:51:44,187 - ERROR - That is an error message
2024-05-18 11:51:44,187 - CRITICAL - It is a vital message

 
app.log

2024-05-18 11:51:44,187 - DEBUG - It is a debug message
2024-05-18 11:51:44,187 - INFO - That is an information message
2024-05-18 11:51:44,187 - WARNING - It is a warning message
2024-05-18 11:51:44,187 - ERROR - That is an error message
2024-05-18 11:51:44,187 - CRITICAL - It is a vital message

Logging Consumer Exercise in a Net Software

On this easy instance, we are going to create a primary net software that logs person exercise utilizing Python’s logging module. This software can have two endpoints: one for logging profitable login makes an attempt and the opposite to doc failed ones (INFO for fulfillment and WARNING for failures).

1. Setting Up Your Setting

Earlier than beginning, arrange your digital atmosphere and set up Flask:

python -m venv myenv

# For Mac
supply myenv/bin/activate

#Set up flask
pip set up flask

2. Making a Easy Flask Software

Whenever you ship a POST request to the /login endpoint with a username and password parameter, the server will test if the credentials are legitimate. If they’re, the logger data the occasion utilizing logger.information() to indicate a profitable login try. Nonetheless, if the credentials are invalid, the logger data the occasion as a failed login try utilizing logger.error().

#Making Imports
from flask import Flask, request
import logging
import os

# Initialize the Flask app
app = Flask(__name__)

# Configure logging
if not os.path.exists('logs'):
    os.makedirs('logs')
log_file="logs/app.log"
logging.basicConfig(filename=log_file, degree=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s")
log = logging.getLogger(__name__)


# Outline route and handler
@app.route('/login', strategies=['POST'])
def login():
    log.information('Acquired login request')
    username = request.type['username']
    password = request.type['password']
    if username == 'admin' and password == 'password':
        log.information('Login profitable')
        return 'Welcome, admin!'
    else:
        log.error('Invalid credentials')
        return 'Invalid username or password', 401

if __name__ == '__main__':
    app.run(debug=True)

3. Testing the Software

To check the applying, run the Python script and entry the /login endpoint utilizing an online browser or a software like curl. For instance:

Check Case 01

 curl -X POST -d "username=admin&password=password" http://localhost:5000/login

 
Output

 
Check Case 02

curl -X POST -d "username=admin&password=wrongpassword" http://localhost:5000/login

 
Output

Invalid username or password

 
app.log

2024-05-18 12:36:56,845 - INFO - Acquired login request
2024-05-18 12:36:56,846 - INFO - Login profitable
2024-05-18 12:36:56,847 - INFO - 127.0.0.1 - - [18/May/2024 12:36:56] "POST /login HTTP/1.1" 200 -
2024-05-18 12:37:00,960 - INFO - Acquired login request
2024-05-18 12:37:00,960 - ERROR - Invalid credentials
2024-05-18 12:37:00,960 - INFO - 127.0.0.1 - - [18/May/2024 12:37:00] "POST /login HTTP/1.1" 200 -

Wrapping Up

And that wraps up this text. I strongly counsel making logging part of your coding routine. It is a good way to maintain your code clear and make debugging simpler. If you wish to dive deeper, you may discover the Python logging documentation for extra options and superior strategies. And if you happen to’re keen to boost your Python expertise additional, be happy to take a look at a few of my different articles:

Kanwal Mehreen Kanwal is a machine studying engineer and a technical author with a profound ardour for knowledge science and the intersection of AI with medication. She co-authored the e book “Maximizing Productiveness with ChatGPT”. As a Google Technology Scholar 2022 for APAC, she champions range and tutorial excellence. She’s additionally acknowledged as a Teradata Range in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower girls in STEM fields.