Best practices and tips on how to do log processing and debugging in Python

Best practices and tips on how to do log processing and debugging in Python

1. Introduction
   Log processing when writing large Python applications and debugging are very important, they can help us track down problems, diagnose errors and improve the code. This article will introduce best practices and techniques for log processing and debugging in Python, as well as specific code examples.
2. Using standard library logging
   Python has a built-in log processing module - logging, which provides a comprehensive set of APIs to process log records, which is very convenient to use. Here is a basic logging example:

import logging

Create a logger

logger = logging.getLogger(__name__)
logger.setLevel (logging.DEBUG)

Create a file handler and write the log to the file

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

Define log format

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

Apply format to handler

file_handler.setFormatter(formatter)

Add handler to logger

logger.addHandler(file_handler)

Write Your code and call the logger object where the log needs to be recorded

logger.debug('This is a debug level log')
logger.info('This is an info level log' )
logger.warning('This is a warning level log')
logger.error('This is an error level log')
logger.critical('This is a critical level log ')

After running the above code, you will see a file named app.log in the same directory, which contains the recorded log information. You can customize the log level, log format, and log output location as needed.

3. Use assertions for debugging
   In addition to logging, assertions are also a very effective debugging technique. You can add some assertions to your code to verify the correctness of the program's logic and data. Here is a simple example:

def divide(x, y):

assert y != 0, "除数不能为0"
return x / y

print(divide(10, 0))

In this example , when the divisor is 0, the assertion will trigger and throw an AssertionError exception. We can easily locate the error location based on the exception information.

4. Interactive debugging using pdb
   The Python standard library also provides a powerful interactive debugger pdb. Insert import pdb; pdb.set_trace() in the code to enter pdb debugging mode at this line of code. You can use a series of pdb commands, such as setting breakpoints, printing variable values, stepping through code, etc., to debug the program line by line. Here is an example:

def add(a, b):

import pdb; pdb.set_trace()
return a + b

print(add(1, 2))

When running this code , when the program is executed to import pdb; pdb.set_trace(), it will enter the pdb debugging mode. You can enter commands to view the values ​​of variables, step through code, and perform other debugging operations.

5. Use third-party libraries for advanced debugging
   In addition to the built-in pdb, there are some third-party libraries that can help us perform more advanced debugging. One of the more popular ones is py debugger (py debugger), which can provide richer debugging functions, such as remote debugging, editing code and reloading, etc. You can use pip to install the py debugger: pip install py debugger.
6. Conclusion
   Log processing and debugging in Python is very important. It can help us track and fix problems and improve the reliability and stability of the program. By using Python's built-in logging module, assertions, and pdb debugger, we can improve debugging efficiency and quickly locate the problem. In addition, you can also use third-party libraries for more advanced debugging operations. Proper application of these techniques and tools in the project will bring great help to our development work.

Reference materials:

1. Python official documentation-logging module: https://docs.python.org/3/library/logging.html
2. Python official documentation-pdb debugger: https://docs.python.org/3/library/pdb.html

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