Debugging skills in Python web development (Part 2)

Debugging skills in Python web development (Part 2)

In the previous article, we briefly learned about some debugging skills in Python web development, including the use of breakpoints, debuggers and other tools to resolve program errors and problems. This article will continue to introduce some debugging tips to help developers debug Python web applications faster and more accurately.

1. Use the log library to record the running status of the program

The log library is a very important tool in Python web development. It can record the running status of the program and output it to a log file. , which is convenient for developers to view and analyze. Commonly used logging libraries in Python include logging and logbook, which provide a variety of logging levels and output formats to meet the needs of different application scenarios.

By setting the log level in the code, we can record the details of each stage of the program and quickly locate errors when problems occur. For example, in the Flask framework, we can enable logs through the following code:

import logging
from logging.handlers import RotatingFileHandler

# 设置日志级别
app.logger.setLevel(logging.DEBUG)

# 定义文件名和文件大小
log_handler = RotatingFileHandler('app.log', maxBytes=10000, backupCount=1)

# 设置日志格式
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
log_handler.setFormatter(formatter)

app.logger.addHandler(log_handler)

This code uses the log function that comes with the Flask framework and configures the log level to DEBUG, which means every detail of the program running will be recorded. The log is output to the app.log file. The file size is 10KB. When the file size exceeds 10KB, it will be automatically backed up to the app.log.1 file. The log format is "[time]-[module name]-[log level]-[log content]" for easy viewing and analysis.

2. Use thread and process debugging tools

In Python web applications, multi-threading, multi-process and other technologies are often used to improve performance. However, these techniques often bring some problems, such as race conditions, deadlocks, etc. In order to solve these problems, developers need to use some thread and process debugging tools.

Python comes with some thread debugging tools, such as threading, queue, etc., which can be used to monitor thread status, locking issues, etc. In addition, Python GIL (Global Interpreter Lock, global interpreter lock) is also an issue we need to pay attention to. GIL is a thread synchronization mechanism in the Python interpreter, which limits only one thread to execute Python code at the same time. This means that when using multi-threading, GIL may cause a waste of CPU and memory resources and affect system performance.

Python also provides some process debugging tools, such as multiprocessing, os, etc., for monitoring process status, pipeline communication, etc. Using these debugging tools can help us locate problems and bottlenecks in the program and make corresponding optimizations and improvements.

3. Use simulation tools to simulate real data environments

In Python web development, we often need to obtain data from databases, API interfaces or other data sources, and Data processing and presentation. However, these data sources may be affected by network, server, database and other environments, resulting in unstable or abnormal data acquisition. In order to solve these problems, we can use some simulation tools to simulate the real data environment for better debugging and testing.

There are some simulation tools in Python, such as mock, betamax, etc., which can simulate HTTP requests, API responses and other data. Using these simulation tools, we can customize the content of requests and responses, simulate various abnormal situations, and facilitate testing and debugging.

For example, use betamax to simulate the process of HTTP request and response:

import requests
import betamax

with betamax.Betamax.configure() as config:
    # 设置cassette保存的位置
    config.cassette_library_dir = 'fixtures/cassettes'
    # 设置模拟器录制模式，当没有匹配的模拟结果时自动生成模拟响应
    config.default_cassette_options['record_mode'] = 'once'
    # 设置请求头
    config.default_cassette_options['match_requests_on'] = ['method', 'path', 'query']

betamax_session = betamax.Betamax(requests.Session())

with betamax_session:
    response = requests.get('http://httpbin.org/get', params={'key': 'value'})
    assert response.status_code == 200

This code uses betamax to simulate an HTTP request and saves the request and response information to the fixtures/cassettes directory. in a file under. The content of the logged response is named _key-default.yaml file. In subsequent tests, if the requested URL and parameters match, the saved response content will be automatically returned. This allows us to test and debug requests and responses in a simulated environment without affecting the real data source.

Summary

This article introduces some debugging skills in Python web development, including the log library to record program running conditions, the use of thread and process debugging tools, and the use of simulation tools to simulate real data environments, etc. . These skills can help us locate and solve problems faster and more accurately, and improve development efficiency and quality. In addition, learning debugging skills requires continuous practice and accumulation of experience. I hope that everyone can continuously improve their debugging skills based on their actual projects.

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