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What do DEBUG, INFO, WARNING, and ERROR mean?
These labels are common, but their precise meanings and numeric values depend on the logging system. In Python’s standard logging guidance, levels run from less severe to more severe as DEBUG, INFO, WARNING, ERROR, and CRITICAL.
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| Python level | Meaning in Python’s documentation | Typical use |
|---|---|---|
| DEBUG | Detailed information useful for diagnosing problems. | Record internal steps or diagnostic context when investigating an issue. |
| INFO | Confirmation that things are working as expected. | Record meaningful milestones in normal operation. |
| WARNING | An unexpected occurrence or possible near-future problem while the software continues to work as expected. | Record recoverable conditions that merit attention, such as low disk space. |
| ERROR | A more serious problem that prevented the software from performing a function. | Record a failed operation that the application can otherwise continue past. |
| CRITICAL | A serious error indicating the program itself may be unable to continue. | Signal a condition that threatens continued operation. |
These definitions come from the Python 3.14.8 Logging HOWTO; they are not a universal contract for every framework. In Python’s level table, the corresponding values are DEBUG 10, INFO 20, WARNING 30, ERROR 40, and CRITICAL 50. Those values belong to Python’s system and should not be assumed to match another platform.
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How does a log-level threshold filter messages?
A threshold is a minimum severity for a record to pass. In Python, setting a logger threshold to INFO allows INFO and more severe records through, while suppressing DEBUG. The logger’s threshold is not necessarily the last filter: each handler can apply its own threshold before sending a record to its destination.
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The flow is: application code creates a record at a level; the logger decides whether it proceeds; one or more handlers route accepted records to destinations such as a console, file, email, or remote service. A handler can further narrow what reaches its destination. Python’s HOWTO describes, for example, sending all messages to a file, errors and above to standard output, and critical messages to email.
This is filtering and routing, not necessarily deletion at the line where the logging call appears. Whether a record is emitted, and where it goes, depends on logger and handler configuration. Python’s documented default threshold is WARNING, so DEBUG and INFO messages are not tracked under that default unless configuration lowers it. Defaults vary by framework.
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Should an application log an error or raise an exception?
Logging an error and raising an exception do different jobs. An exception signals that an operation failed and allows the program or caller to handle that failure. Logging records information for operators or diagnosis. Python’s HOWTO advises that command-line output may use print(), while an error condition generally calls for raising an exception. Logging an error is appropriate when suppressing the error is intentional—for example, a long-running server handles a failed request and continues rather than exiting.
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Why do applications rotate log files?
Logs accumulate as applications run. Rotation manages file growth and retained history, independently of severity: it can move logging to a new file after a configured size or elapsed time, then keep a limited set of older files or discard them according to the policy. It does not decide whether an event is DEBUG or ERROR.
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Size-based rotation
Use size-based rotation when the key operational limit is how large a file may become. Python’s RotatingFileHandler can open a new file after a maximum size is reached and retain a configured number of backups. With the backup count and size threshold chosen, the number of files and approximate disk use can be bounded.
Time-based rotation
Use time-based rotation when separate files aligned to hours, days, or another time interval make operations or review easier. Python provides TimedRotatingFileHandler. The interval and retention settings should reflect the history needed for troubleshooting and the disk budget; the official documentation does not prescribe one universally correct threshold or retention period.
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How should retention and deployment shape the design?
- Choose a trigger: set a size limit if file growth is the main constraint, or a time interval if calendar-aligned log segments are more useful.
- Set retention deliberately: balance disk capacity against how far back operators may need to investigate. A backup count or time-retention policy is a configuration choice, not a universal best practice.
- Check the writer model: a simple file-handler rotation setup should not be assumed safe for multiple concurrent processes.
The Python Logging Cookbook warns that web servers such as Gunicorn or uWSGI may run multiple worker processes, and advises against having those workers write directly through file handlers in the application. Its recommended pattern is to send records to a socket listener in a separate process, which owns the file output. That separates concurrent application workers from file management.
Why can’t severity numbers be compared across logging systems?
Numeric severity is system-specific. Python assigns larger numbers to more severe levels, from DEBUG 10 to CRITICAL 50. By contrast, RFC 5424 syslog numbers severities from 0 (Emergency) through 7 (Debug), with lower numbers representing greater severity. Do not compare the numbers as if they shared a scale; when forwarding logs between systems, map levels by meaning and verify the receiving system’s conventions.
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