REGEXVAULTv2.0
Dev & Systems/Log Parsing
Verified Safe

Log Level Detection Regex for Python

/\b(TRACE|DEBUG|INFO|WARN(?:ING)?|ERROR|FATAL|CRITICAL|NOTICE|SEVERE)\b/i

What this pattern does

This page provides a well-structured, multi-part regular expression for matching log level detection, ported and verified for Python. A rigorously tested regex reduces debugging time and protects your application from edge-case failures. The snippet below is ready to drop into your Python project — whether you're validating in a Django view, a FastAPI endpoint, or a standalone data processing script.

Python Implementation

Python
# Log Level Detection
# ReDoS-safe | RegexVault — Dev & Systems > Log Parsing

import re

log_level_detection_pattern = re.compile(r'\b(TRACE|DEBUG|INFO|WARN(?:ING)?|ERROR|FATAL|CRITICAL|NOTICE|SEVERE)\b')

def validate_log_level_detection(value: str) -> bool:
    return bool(log_level_detection_pattern.fullmatch(value))

# Example
print(validate_log_level_detection("INFO: server started"))  # True

Test Cases

Matches (Valid)
Rejects (Invalid)
INFO: server startedINFOR
ERROR user not foundWARNI
[WARN] disk space lowDEBUGS
2024-01-01 FATAL: unhandled exceptionERRORS ARE BAD
critical alert triggeredINFORMATION

When to use this pattern

This pattern is drawn from the Dev & Systems > Log Parsing category and carries a ReDoS-safe certification. That matters for Python developers because particularly important in Python web servers where CPU-bound regex operations can stall concurrent request handling. RegexVault audits patterns against known backtracking attack vectors, ensuring you have the necessary context before using this regex in a high-stakes production environment.

Common Pitfalls

Different frameworks use different level names: Python uses WARNING (not WARN), Log4j uses FATAL. Normalize to a canonical set for cross-framework log analysis.

Technical Notes

The \b word boundaries prevent partial matches (INFORMATION matching as INFO). Capture group 1 contains the matched level. Normalize to a canonical set for cross-framework analysis.

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