import time
import logging
from functools import wraps

def retry_on_failure(max_retries=3, delay=1, backoff=2):
    """재시도 데코레이터"""
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            retries = 0
            while retries < max_retries:
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    retries += 1
                    if retries >= max_retries:
                        logging.error(f"최종 실패: {func.__name__}, 오류: {e}")
                        raise e
                    
                    wait_time = delay * (backoff ** (retries - 1))
                    logging.warning(f"재시도 {retries}/{max_retries}: {wait_time}초 대기")
                    time.sleep(wait_time)
            
            return None
        return wrapper
    return decorator

@retry_on_failure(max_retries=3, delay=2, backoff=2)
def collect_data_from_api(endpoint):
    """API 데이터 수집 with 재시도"""
    # API 호출 로직
    pass

def validate_data_quality(df, validation_rules):
    """데이터 품질 검증"""
    validation_results = {}
    
    for rule_name, rule_func in validation_rules.items():
        try:
            result = rule_func(df)
            validation_results[rule_name] = {
                'passed': result,
                'timestamp': datetime.now().isoformat()
            }
        except Exception as e:
            validation_results[rule_name] = {
                'passed': False,
                'error': str(e),
                'timestamp': datetime.now().isoformat()
            }
    
    return validation_results

# 검증 규칙 예시
validation_rules = {
    'completeness_check': lambda df: df.isnull().sum().sum() / (len(df) * len(df.columns)) < 0.05,
    'duplicate_check': lambda df: df.duplicated().sum() / len(df) < 0.01,
    'range_check': lambda df: ((df['age'] >= 0) & (df['age'] <= 120)).all() if 'age' in df.columns else True
}