Python logging done right
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sure! python's logging module is a powerful tool for tracking events that happen in your application. when done right, logging can help you debug issues, monitor the performance of your application, and provide valuable insights into its behavior.
here are some best practices for using python logging effectively:
1. **use different logger instances**: create separate logger instances for different parts of your application. this allows you to control the logging behavior for each component independently.
2. **set the logging level**: use the `setlevel` method to set the minimum logging level for each logger. this way, you can filter out less important log messages and only focus on the ones that matter.
3. **configure logging format**: customize the format of your log messages using the `basicconfig` method. you can include timestamps, log levels, module names, and more to make your logs more informative.
4. **choose the right handler**: use different handlers like filehandler, streamhandler, rotatingfilehandler, etc., to send log messages to different destinations such as files, streams, or even email.
5. **avoid configuring logging in the code**: instead of hardcoding logging configurations in your code, consider using a configuration file (e.g., json, yaml) or environment variables to configure logging settings.
6. **handle exceptions**: use try-except blocks to catch and log exceptions. this helps you track errors and exceptions that occur during the execution of your code.
7. **log only what's necessary**: avoid logging sensitive information like passwords or api keys. stick to logging relevant information that helps in debugging and monitoring your application.
here's an example demonstrating how to implement these best practices in python:
in this example, we create two logger instances, set their logging levels, configure a logging format, add a file handler to send log messages to a file, and demonstrate logging error and info messages within a ...
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