Backend Development
Python Tutorial
How does Python's logging module provide a flexible framework for emitting log messages?
How does Python's logging module provide a flexible framework for emitting log messages?
Jun 05, 2025 am 12:13 AM
Python's logging module provides flexible logging capabilities through log levels, processors, formatting and hierarchical loggers. It supports five log levels (DEBUG, INFO, WARNING, ERROR, CRITICAL), allowing developers to classify information according to severity and control the output content by setting a global level; for example, only WARNING and above logs are displayed in production environments to keep it simple. In addition, logs can be sent to different targets, such as files, mails, or consoles by adding multiple processors, each processor can independently configure levels and formats; for example, one processor writes all DEBUG and above logs to a file, and the other displays WARNING and above levels only in the console. At the same time, formatter is used to customize log formats, such as including fields such as timestamps, log level, module name, etc., to improve log readability. Finally, the named logger supports organizing log systems according to the code structure, realizing independent configuration of different modules. For example, myapp.moduleA and myapp.moduleB can set different log levels separately to meet the diverse needs in complex projects.
Python's logging module is built to help developers track events in their code—like errors, warnings, or general status updates—in a structured and customized way. Instead of scattering print statements throughout your code, logging gives you a consistent framework that scales well from simple scripts to complex applications.
Here's how it offers that flexibility:
Multiple Log Levels for Different Needs
One of the core features is the use of log levels. These let you category messages by severity: DEBUG , INFO , WARNING , ERROR , and CRITICAL . You can control which level of messages get recorded or displayed.
For example:
- Use
logging.debug()for detailed info useful during development. - Use
logging.warning()when something unexpected happens but isn't critical. - Use
logging.error()orlogging.critical()when things go wrong and action may be needed.
You can set the logging level globally so only messages at or above that level are processed.
Say you're in production—you might only want to see WARNING and above:
logging.basicConfig(level=logging.WARNING)
This keeps logs cleaner and more relevant.
Handlers Let You Send Logs Wherever You Want
By default, logs just show up on the console. But what if you want them written to a file? Or sent over the network? Handlers make this possible.
You can attach multiple handlers to a logger. For instance:
- One handler writes logs to a file.
- Another sends error logs to an email address via SMTP.
- Yet another outputs logs to the console with different formatting.
Each handler can also have its own level. So maybe you log everything to a file ( DEBUG and up), but only show warnings and errors in the console.
Example of adding a file handler:
handler = logging.FileHandler('app.log') formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') handler.setFormatter(formatter) logging.getLogger().addHandler(handler)
Flexible Formatting for Readable Output
Log messages don't all have to look the same. With formatters, you define what each message includes—like timestamp, logger name, severity level, or even the function name where the log originated.
Want to include the time and level in every message?
logging.basicConfig(format='%(asctime)s - %(levelname)s: %(message)s')
Or maybe you want more context in debug logs (like the module name and line number) and keep it simpler in error logs. Formatters gives you that control per handler.
Loggers Organized Like Your Code Structure
Python's logging system supports hierarchical loggers. That means you can create named loggers like myapp.moduleA and myapp.moduleB , and configure them individually or as a group ( myapp ).
This helps in large apps where different components might need different logging behavior. For example:
- Set
myapp.databaseto log atDEBUGlevel for troubleshooting. - Keep
myapp.uiatINFOto avoid noise unless needed.
Getting a named logger is easy:
logger = logging.getLogger('myapp.moduleA') logger.debug('This only shows if moduleA's logging level allows it.')
All these pieces—levels, handlers, formatters, and named loggers—work together so you can build a logging setup that fits your exact needs without rewriting the wheel. It's not flashy, but once you understand how they connect, it becomes a powerful tool in debugging and monitoring applications.
That's the basic idea—and honestly, most of what you need to start using logging effectively.
The above is the detailed content of How does Python's logging module provide a flexible framework for emitting log messages?. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undress AI Tool
Undress images for free
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Clothoff.io
AI clothes remover
Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
PHP calls AI intelligent voice assistant PHP voice interaction system construction
Jul 25, 2025 pm 08:45 PM
User voice input is captured and sent to the PHP backend through the MediaRecorder API of the front-end JavaScript; 2. PHP saves the audio as a temporary file and calls STTAPI (such as Google or Baidu voice recognition) to convert it into text; 3. PHP sends the text to an AI service (such as OpenAIGPT) to obtain intelligent reply; 4. PHP then calls TTSAPI (such as Baidu or Google voice synthesis) to convert the reply to a voice file; 5. PHP streams the voice file back to the front-end to play, completing interaction. The entire process is dominated by PHP to ensure seamless connection between all links.
How to use PHP combined with AI to achieve text error correction PHP syntax detection and optimization
Jul 25, 2025 pm 08:57 PM
To realize text error correction and syntax optimization with AI, you need to follow the following steps: 1. Select a suitable AI model or API, such as Baidu, Tencent API or open source NLP library; 2. Call the API through PHP's curl or Guzzle and process the return results; 3. Display error correction information in the application and allow users to choose whether to adopt it; 4. Use php-l and PHP_CodeSniffer for syntax detection and code optimization; 5. Continuously collect feedback and update the model or rules to improve the effect. When choosing AIAPI, focus on evaluating accuracy, response speed, price and support for PHP. Code optimization should follow PSR specifications, use cache reasonably, avoid circular queries, review code regularly, and use X
How to develop AI intelligent form system with PHP PHP intelligent form design and analysis
Jul 25, 2025 pm 05:54 PM
When choosing a suitable PHP framework, you need to consider comprehensively according to project needs: Laravel is suitable for rapid development and provides EloquentORM and Blade template engines, which are convenient for database operation and dynamic form rendering; Symfony is more flexible and suitable for complex systems; CodeIgniter is lightweight and suitable for simple applications with high performance requirements. 2. To ensure the accuracy of AI models, we need to start with high-quality data training, reasonable selection of evaluation indicators (such as accuracy, recall, F1 value), regular performance evaluation and model tuning, and ensure code quality through unit testing and integration testing, while continuously monitoring the input data to prevent data drift. 3. Many measures are required to protect user privacy: encrypt and store sensitive data (such as AES
python seaborn jointplot example
Jul 26, 2025 am 08:11 AM
Use Seaborn's jointplot to quickly visualize the relationship and distribution between two variables; 2. The basic scatter plot is implemented by sns.jointplot(data=tips,x="total_bill",y="tip",kind="scatter"), the center is a scatter plot, and the histogram is displayed on the upper and lower and right sides; 3. Add regression lines and density information to a kind="reg", and combine marginal_kws to set the edge plot style; 4. When the data volume is large, it is recommended to use "hex"
How to use PHP combined with AI to analyze video content PHP intelligent video tag generation
Jul 25, 2025 pm 06:15 PM
The core idea of PHP combining AI for video content analysis is to let PHP serve as the backend "glue", first upload video to cloud storage, and then call AI services (such as Google CloudVideoAI, etc.) for asynchronous analysis; 2. PHP parses the JSON results, extract people, objects, scenes, voice and other information to generate intelligent tags and store them in the database; 3. The advantage is to use PHP's mature web ecosystem to quickly integrate AI capabilities, which is suitable for projects with existing PHP systems to efficiently implement; 4. Common challenges include large file processing (directly transmitted to cloud storage with pre-signed URLs), asynchronous tasks (introducing message queues), cost control (on-demand analysis, budget monitoring) and result optimization (label standardization); 5. Smart tags significantly improve visual
PHP integrated AI emotional computing technology PHP user feedback intelligent analysis
Jul 25, 2025 pm 06:54 PM
To integrate AI sentiment computing technology into PHP applications, the core is to use cloud services AIAPI (such as Google, AWS, and Azure) for sentiment analysis, send text through HTTP requests and parse returned JSON results, and store emotional data into the database, thereby realizing automated processing and data insights of user feedback. The specific steps include: 1. Select a suitable AI sentiment analysis API, considering accuracy, cost, language support and integration complexity; 2. Use Guzzle or curl to send requests, store sentiment scores, labels, and intensity information; 3. Build a visual dashboard to support priority sorting, trend analysis, product iteration direction and user segmentation; 4. Respond to technical challenges, such as API call restrictions and numbers
How to develop AI-based text summary with PHP Quick Refining Technology
Jul 25, 2025 pm 05:57 PM
The core of PHP's development of AI text summary is to call external AI service APIs (such as OpenAI, HuggingFace) as a coordinator to realize text preprocessing, API requests, response analysis and result display; 2. The limitation is that the computing performance is weak and the AI ecosystem is weak. The response strategy is to leverage APIs, service decoupling and asynchronous processing; 3. Model selection needs to weigh summary quality, cost, delay, concurrency, data privacy, and abstract models such as GPT or BART/T5 are recommended; 4. Performance optimization includes cache, asynchronous queues, batch processing and nearby area selection. Error processing needs to cover current limit retry, network timeout, key security, input verification and logging to ensure the stable and efficient operation of the system.
python list to string conversion example
Jul 26, 2025 am 08:00 AM
String lists can be merged with join() method, such as ''.join(words) to get "HelloworldfromPython"; 2. Number lists must be converted to strings with map(str, numbers) or [str(x)forxinnumbers] before joining; 3. Any type list can be directly converted to strings with brackets and quotes, suitable for debugging; 4. Custom formats can be implemented by generator expressions combined with join(), such as '|'.join(f"[{item}]"foriteminitems) output"[a]|[
