Python logging: The Comprehensive guide and Best practices

Introduction to Python Logging

A built-in library called Python logging allows you to record messages from your application. Debugging and detecting production issues can be accomplished using these log messages.

The logging library provides a variety of log message levels, from debug messages, which are typically used during development, to critical errors, which indicate application failures. Custom log levels can be defined to suit your needs.

Getting Started with Python Logging

Here are some Log message levels which you should familiarize yourself with before diving into Python logging.

Log Message levels

Debug: These are used to provide detailed information, which is usually only appropriate when identifying issues.

Information: These are used to verify that everything is functioning as expected. These are used as a sign that something unexpected happened or as a sign that there will be an issue soon.

Error: This indicates that the software was unable to complete a task because of a more serious issue.

Critical: The program may not be able to continue if this error occurs.

However, the above warnings are sufficient to carry out error logging. You can add as many distinct warnings as you want.

To begin with, You must import the logging module into your code to begin using Python logging. Then, you can configure your logger's basic settings using the basicConfig() function. This function accepts several optional arguments that can be used to customize the way your logs are output. For instance, You could specify the log level, format, and file.

For example, the following code will configure the logging library to write log messages

import logging

logging.basicConfig(level = logging.INFO, format = '%(asctime)s-%(name)s -%(levelname)s-%(message)s');

In this example, we are setting the log level to INFO, which means that the logger will only output messages with a log level of INFO or higher. We are also specifying a log format that includes the time the log message was emitted, the logger's name, the log level, and the log message itself.

logging.debug('This is a debug message')

You can also use the log() function to output a log message at a specific log level:

logging.log(logging.ERROR, 'This is an error message')

Best Practices of Python logging

Use Appropriate Log Levels

Choosing the proper log level for each message you log in is crucial. Use the appropriate log level for each message when using Python's multiple log levels, which range from DEBUG to CRITICAL. Keeping your logs organized and finding the information you need is easier when you use the proper log level.

Include Contextual Information in Log Messages

Including contextual information in your log messages can make it easier to understand what is happening in your code. For example, you might include the current function name, the line number, or the values of variables. You can use logging. LogRecord objects to access this information and include it in your log messages.

Here is an example of how to include contextual information in a log message:

import logging

def some_function():
    variable = 'foo'
    logging.debug('The value of variable is %s', variable)

In this example, we are using the %s placeholder in the log message to include the value of the variable in the log message.

Use a Separate Log File

It is often a good idea to write your logs to a separate file, rather than outputting them to the console. This makes it easier to review and analyze your logs, and it can also help to keep your console output clean and organized. You can use the filename argument in the basicConfig() function to specify the log file.

Here is an example of how to write logs to a file:

import logging
logging.basicConfig(level=logging.INFO, filename='py_app.log')

In this example, we are specifying a log file named app.log and setting the log level to INFO. All log messages with a log level of INFO or higher will be written to this file.

It is a good idea to rotate the log files you use when writing your logs to a file regularly. By doing this, the log files are kept from growing too big and taking up too much disc space. When log files grow too large or get too old, the RotatingFileHandler class from Python logging can be used to automatically rotate them.

Here is an example of how to use the RotatingFileHandler class:

import logging
from logging.handlers import RotatingFileHandler

log_handler = RotatingFileHandler('py_app.log', maxBytes=10000, backupCount=3)
log_handler.setLevel(logging.INFO)
log_handler.setFormatter(logging.Formatter('%(asctime)s-%(name)s-%(levelname)s-%(message)s'))

logger = logging.getLogger('app')
logger.addHandler(log_handler)

In this example, we're building a RotatingFileHandler that will generate a fresh log file once the existing one has grown to 10,000 bytes in size. Three backup log files are required to be kept, according to the backupCount parameter. The handler's log level is specified using the setLevel() method and the log format is specified using the setFormatter() method.

Once the RotatingFileHandler has been set up, we can log messages, as usual, using the logger object. According to the configuration specified in the RotatingFileHandler the log messages will be written to the log file and rotated.

Use a consistent log format

A best practice for Python logging is to always use the same log format. This will help to organize your logs and make them simpler to parse and comprehend. The way that log messages are organized and formatted within your application is referred to as a consistent log format.

With Python logging, you can use a consistent log format by passing the format argument to the basicConfig() function. This argument accepts a string that contains special codes that define the log format. As an illustration, the code %(asctime)s indicates the time the log message was sent, and%(name)s indicates the name of the logger.

Here is an example of how to use a consistent log format in Python logging:

import logging

logging.basicConfig(level=logging.INFO, format='[%(levelname)s] %(asctime)s - %(message)s')

In this instance, we're defining a log format that consists of the log level enclosed in square brackets, the time the log message was sent, and the actual log message.

It can be simpler to parse and comprehend your logs if your application uses a consistent log format. As you can use the log format to recognize the elements of the log message, it can also make it simpler to search for particular log messages.

Use the logging module, rather than print() statements

The following are some justifications for using the logging module in your Python applications:

1. Log levels: To make it simpler to organize and filter your logs, the logging module lets you specify the log level for each message you log. You would either need to manually add log levels to your messages if you used print() statements or use different print() statements for each log level. The logging module enables you to output your logs to a variety of locations, including the console, a file, or a network socket. This can be helpful if you want to output your logs to various locations based on the log level or if you want to send your logs to a centralized logging system.

2. Exception handling: You can log exceptions and tracebacks using the logging module, which helps debug and identify code issues.

3. Performance: The logging module is generally more efficient than using print() statements, especially when logging large numbers of messages. The logging module uses a queue and worker thread to handle log messages, which can help to improve the performance of your application.

Avoid including sensitive information in log messages

Sensitive information is any data that shouldn't be shared with unauthorized parties, like passwords, private information, or proprietary business data. The possibility of unauthorized access to log files, including sensitive information in log messages can be a security risk. It is possible for someone to access a log file containing sensitive data if it is kept on a public server, for instance, and view the sensitive data.

You should be aware of the data you are logging and make sure that you are not disclosing sensitive information in your logs to prevent including sensitive information in log messages.

Additionally, you should think about putting security measures in place to safeguard your log files, like limiting access to authorized users or keeping the log files on a secure server.

Here are a few best practices for avoiding sensitive information in log messages:

1. Redact sensitive data: If you must log sensitive data for debugging or troubleshooting reasons, you might want to think about redacting the sensitive data first. For instance, you could use asterisks to replace sensitive information or a hash function to hide it.

2. Use a separate log file for sensitive data: Instead of logging sensitive data alongside non-sensitive data, you might want to use a separate log file. This can lessen the chance that private information will unintentionally leak into the logs.

3. Use a secure logging system: To gather and manage your logs, think about using a secure logging system like Elasticsearch or Splunk. To help safeguard your log data, these systems typically offer secure storage and access controls.

You can contribute to ensuring that your logs do not reveal sensitive information and that your application is secure by adhering to these best practices.

Use a centralized logging system

Using a centralized logging system in Python logging refers to the practice of collecting and managing log data from multiple sources in a central location. A centralized logging system can be used to store, search, and analyze log data from multiple servers, applications, or devices.

Using a centralized logging system in Python logging has several advantages:

1. Consolidated view of log data: By allowing you to view log data from various sources in one location, a centralized logging system can help you better understand what's going on in your environment.

2. Improved search and analysis: A centralized logging system frequently comes with strong search and analysis tools, which can make it simpler to locate and comprehend certain log messages or patterns in your log data.

3. Scalability: In distributed environments with numerous servers or devices, a centralized logging system can scale to handle large volumes of log data.

4. Security: To help safeguard your log data, a centralized logging system can offer secure storage and access controls.

In Python logging, you can use a logging handler that can send log data to the centralized system if you want to use a centralized logging system. For instance, log data can be sent to an Elasticsearch server using the LogstashHandler provided by the logstash library.

Here is an example of how to use the LogstashHandler to send log data to Elasticsearch:

import logging
from logstash.handler import LogstashHandler

logstash_handler = LogstashHandler(host='localhost',port=5044,version=1)
logstash_handler.setLevel(logging.INFO)

Conclusion

Overall, Python logging is a powerful tool that allows developers to log messages in their code and track the execution of their programs. When used effectively, logging can help to improve the reliability and maintainability of your code. However, it is important to use logging best practices to ensure that your logs are organized and useful.

Some best practices for using Python logging include using appropriate log levels, including contextual information in log messages, using a separate log file, rotating log files, using a consistent log format, using the logging module instead of print() statements, avoiding sensitive information in log messages, using log levels wisely, and using a centralized logging system. By following these best practices, you can ensure that your logs are useful and informative, and you can improve the reliability and maintainability of your code.