from dataclasses import dataclass, field
from datetime import datetime
import logging
from typing import List

logger = logging.getLogger("metrics")

@dataclass
class MessageMetrics:
    topic: str
    processing_time_ms: float
    error: bool = False
    error_msg: str = ""
    timestamp: datetime = field(default_factory=datetime.now)

message_metrics: List[MessageMetrics] = []

def record_metric(topic: str, processing_time_ms: float, error: bool = False, error_msg: str = ""):
    metric = MessageMetrics(
        topic=topic,
        processing_time_ms=processing_time_ms,
        error=error,
        error_msg=error_msg
    )
    message_metrics.append(metric)
    if len(message_metrics) > 1000:
        message_metrics.pop(0)
    
    if error:
        logger.error(f"[METRIC ERROR] {topic}: {error_msg} ({processing_time_ms:.1f}ms)")
    elif processing_time_ms > 100:
        logger.warning(f"[METRIC SLOW] {topic}: {processing_time_ms:.1f}ms (>100ms)")

def get_metrics_summary() -> dict:
    if not message_metrics:
        return {"message": "No metrics recorded yet"}
    
    total_count = len(message_metrics)
    error_count = sum(1 for m in message_metrics if m.error)
    avg_time = sum(m.processing_time_ms for m in message_metrics) / total_count
    
    return {
        "total_messages": total_count,
        "error_count": error_count,
        "error_rate": error_count / total_count if total_count > 0 else 0.0,
        "avg_processing_time_ms": round(avg_time, 2),
        "last_update": message_metrics[-1].timestamp.isoformat()
    }