Best Logging Practices in Python

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If you’re like me, you love writing code but sometimes struggle with keeping track of what’s going on in your programs. That’s where logging comes in it’s a powerful tool that can help you debug and optimize your code while also providing insights into how your application is performing.

But before we dive into the best practices for Python logging, why logging matters. First, it allows us to keep track of what our program is doing at any given time. This can be especially helpful when debugging issues or trying to identify performance bottlenecks. Secondly, logs provide valuable insights that we can use to improve the overall quality and reliability of our software.

So how do we implement proper logging practices in Python? Here are 10 best practices to follow:

1. Use a logger object instead of printing directly to console or file. This allows us to easily control the level of detail that is logged, as well as the destination where logs are sent (console, file, etc.).

2. Set up logging levels based on severity. The most common levels include DEBUG, INFO, WARNING, ERROR, and CRITICAL. By default, set your logger to log at a level of WARNING or higher. This will help you avoid being overwhelmed by too much information in your logs.

3. Use formatted strings for logging messages instead of concatenating them with the + operator. This can improve performance and make it easier to read and understand our logs. For example:

# Import the logging module
import logging

# Set the logging level to WARNING
logging.basicConfig(level=logging.WARNING)

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

# Use formatted strings for logging messages
logger.debug("Starting program...") # Changed to logger.debug(f"Starting program...") to use formatted string

# Create a function to add two numbers
def add(x, y):
    # Log the input values
    logger.info(f"Adding {x} and {y}...") # Changed to logger.info(f"Adding {x} and {y}...") to use formatted string

    # Add the numbers
    result = x + y

    # Log the result
    logger.info(f"Result: {result}") # Changed to logger.info(f"Result: {result}") to use formatted string

    # Return the result
    return result

# Call the add function
add(5, 10)

4. Include relevant context in your log messages, such as function names or line numbers. This will help you quickly identify where issues are occurring within your code.

5. Use a consistent logging format across all of your projects and applications. This can make it easier to compare logs from different sources and identify trends over time.

6. Rotate log files regularly to prevent them from becoming too large or filling up disk space. You can use tools like Logrotate or TimedRotatingFileHandler to automate this process.

7. Use a centralized logging system if you have multiple applications or services running on the same server. This will allow you to easily monitor and manage logs across your entire infrastructure.

8. Avoid using log messages for debugging purposes, as these can clutter up your logs and make it harder to identify important events. Instead, use a separate tool like pdb (Python Debugger) or IDE debuggers for more detailed debugging.

9. Test your logging configuration thoroughly before deploying your application in production. This will help you catch any issues early on and ensure that logs are being properly recorded and sent to their intended destination.

10. Finally, review your logs regularly to identify trends or patterns that can inform product decisions. By analyzing our logs over time, we can gain valuable insights into how our application is performing in real-world scenarios.

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