Calculate Error Rate from Log Stream in Python

import re
from collections import deque

def error_rate_from_log_stream(message):
    log_pattern = r'^\[(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2})\] (ERROR|INFO|DEBUG): (.*)$'
    recent_entries = deque(maxlen=100)
    error_count = 0
    total_count = 0

    for line in message.strip().split('\n'):
        match = re.match(log_pattern, line)
        if match:
            total_count += 1
            recent_entries.append((match.group(1), match.group(2), match.group(3)))
            if match.group(2) == 'ERROR':
                error_count += 1

    current_error_rate = (error_count / total_count * 100) if total_count else 0.0
    return current_error_rate, error_count, total_count, list(recent_entries)

if __name__ == "__main__":
    mock_logs = """\
[2025-03-15 10:30:01] INFO: Application started
[2025-03-15 10:30:02] ERROR: Database connection failed
[2025-03-15 10:30:03] INFO: Retrying connection
[2025-03-15 10:30:04] DEBUG: Cache refreshed
[2025-03-15 10:30:05] ERROR: Timeout on API call
[2025-03-15 10:30:06] INFO: Request completed
"""
    rate, errors, total, entries = error_rate_from_log_stream(mock_logs)
    print(f"Total log entries: {total}")
    print(f"Error count: {errors}")
    print(f"Error rate: {rate:.2f}%")
    print(f"\nRecent entries (last {len(entries)}):")
    for ts, level, msg in entries:
        print(f"  [{ts}] {level}: {msg}")