Logging levels help categorize log messages by severity, ensuring developers and system administrators can efficiently filter and analyze logs. This article explores different logging levels, their purposes, and best practices.
Table of Contents
What Are Logging Levels?
Logging levels define the importance and severity of log messages. These levels help in managing log verbosity and ensuring that critical messages are not lost among less significant details. Most logging frameworks support a standardized set of logging levels, although specific implementations may vary slightly.
Order of severity:
Why do we need so many log levels?
- Each level represents a different level of urgency and severity.
- If the log levels are used properly in your application all you need is to look at the severity first.
- Maintaining the different levels helps one to deal with them efficiently.
Common Logging Levels
DEBUG
Purpose:
- Used for detailed diagnostic information.
- Helps in debugging issues by providing granular insights.
When to Use:
- When developing or troubleshooting an application.
- To log detailed information about variable states, method calls, and execution paths.
Example:
import logging
logging.basicConfig(level=logging.DEBUG)
logging.debug("Fetching user data from API.")INFO
Purpose:
- Provides general operational information.
- Confirms that things are working as expected.
When to Use:
- To log high-level application milestones (e.g., service startup, request handling).
- For routine operations that should be trackable.
Example:
logging.info("User logged in successfully.")WARNING
Purpose:
- Indicates a potential problem that may not cause immediate failure but requires attention.
- Helps in tracking unusual or unexpected events.
When to Use:
- When an API response is slower than expected.
- When a deprecated function is being used.
Example:
logging.warning("Disk usage at 80%. Consider cleaning up logs.")ERROR
Purpose:
- Highlights errors that prevent certain functionalities from executing.
- Indicates that something has gone wrong but the application can continue running.
When to Use:
- When a request fails due to an invalid user input or missing file.
- When an expected external dependency is unavailable.
Example:
logging.error("Failed to connect to database. Retrying…")CRITICAL
Purpose:
- Denotes serious failures that could lead to system shutdown.
- Requires immediate attention.
When to Use:
- When a service is completely unavailable.
- When an unrecoverable error occurs.
Example:
logging.critical("Server out of memory! Immediate action required.")Best Practices for Using Logging Levels
- Use Appropriate Levels: Avoid excessive DEBUG logs in production and ensure ERROR and CRITICAL logs are well-monitored.
- Configure Logging Properly: Set different log levels for development, testing, and production environments.
- Use Log Aggregation Tools: Tools like ELK Stack (Elasticsearch, Logstash, Kibana) or Splunk help in managing large-scale logs efficiently.
- Avoid Logging Sensitive Data: Do not log passwords, API keys, or personally identifiable information (PII).
- Include Contextual Information: Logs should contain relevant details like timestamps, request IDs, and user identifiers to aid in debugging.
Conclusion
Understanding and effectively using logging levels can significantly enhance application maintainability and troubleshooting efficiency. By categorizing logs appropriately, developers can ensure that critical information is not lost while maintaining manageable log verbosity.