"""服务器监控采集服务。

以较高频率采样系统指标（CPU/内存），并以较低频率进行下采样后：
- 通过 WebSocket 广播给前端
- 写入 TimescaleDB（server_metrics hypertable）
"""

import asyncio
import time
import logging
import psutil
import socket
import platform
from datetime import datetime, timezone
from sqlalchemy import insert
from sqlalchemy.ext.asyncio import async_sessionmaker

from backend.database.schema import server_metrics
from backend.services.broadcaster import Broadcaster

logger = logging.getLogger("backend.monitor")

class ServerMonitorService:
    """服务器资源监控服务（采样 + 下采样 + 广播 + 落库）。"""

    def __init__(self, session_factory: async_sessionmaker, broadcaster: Broadcaster):
        self._session_factory = session_factory
        self._broadcaster = broadcaster
        self._host_name = socket.gethostname()
        self._running = False
        self._task = None
        self._sample_interval = 1.0 / 50.0  # 50Hz (20ms)
        self._report_interval = 1.0 / 10.0  # 10Hz (100ms)
        self._last_report_time = 0.0
        
        # Buffer for downsampling
        self._buffer_cpu = []
        self._buffer_mem = []

    async def start(self):
        """启动监控循环（幂等）。"""
        if self._running:
            return
        self._running = True
        self._task = asyncio.create_task(self._run_loop())
        logger.info("ServerMonitorService started")

    async def stop(self):
        """停止监控循环并等待任务结束。"""
        self._running = False
        if self._task:
            try:
                await self._task
            except asyncio.CancelledError:
                pass
        logger.info("ServerMonitorService stopped")

    async def _run_loop(self):
        """采样循环：50Hz 采样，10Hz 报告。"""
        loop = asyncio.get_running_loop()
        next_time = loop.time()
        
        while self._running:
            # High frequency sampling (50Hz)
            # psutil.cpu_percent(interval=None) is non-blocking
            cpu_percent = psutil.cpu_percent(interval=None)
            mem = psutil.virtual_memory()
            
            self._buffer_cpu.append(cpu_percent)
            self._buffer_mem.append(mem)
            
            current_time = loop.time()
            
            # Check if it's time to report (10Hz)
            if current_time - self._last_report_time >= self._report_interval:
                await self._process_and_report()
                self._last_report_time = current_time
                self._buffer_cpu.clear()
                self._buffer_mem.clear()

            # Precise timing control
            next_time += self._sample_interval
            sleep_time = next_time - loop.time()
            if sleep_time > 0:
                await asyncio.sleep(sleep_time)
            else:
                # If we are lagging, yield execution but don't sleep
                await asyncio.sleep(0)

    async def _process_and_report(self):
        """对采样缓冲区做下采样，并完成广播与落库。"""
        if not self._buffer_cpu:
            return

        # Downsampling: Calculate average of buffered samples
        avg_cpu = sum(self._buffer_cpu) / len(self._buffer_cpu)
        
        # Take the latest memory reading (memory doesn't fluctuate as fast as CPU)
        last_mem = self._buffer_mem[-1]
        
        payload = {
            "ts": datetime.now(timezone.utc).isoformat(),
            "host_name": self._host_name,
            "cpu_usage_percent": {
                "total": round(avg_cpu, 2),
                # Note: per-core usage is expensive to query at 50Hz, so we only track total here
                # or we could sample per-core at lower frequency
            },
            "memory_usage_bytes": {
                "total": last_mem.total,
                "available": last_mem.available,
                "used": last_mem.used,
                "percent": last_mem.percent
            },
            "disk_usage_bytes": {} # Optional: disk usage changes slowly, maybe check every 1s
        }

        # 1. Broadcast via WebSocket (10Hz)
        await self._broadcaster.broadcast_json({
            "type": "server.metrics",
            "payload": payload
        })

        # 2. Persist to Database (10Hz)
        # Note: In production, consider batching inserts further (e.g., every 1s)
        # to reduce DB load, but 10Hz single insert is manageable for TimescaleDB.
        async with self._session_factory() as session:
            try:
                stmt = insert(server_metrics).values(
                    ts=datetime.fromisoformat(payload["ts"]),
                    host_name=payload["host_name"],
                    cpu_usage_percent=payload["cpu_usage_percent"],
                    memory_usage_bytes=payload["memory_usage_bytes"],
                    disk_usage_bytes=payload["disk_usage_bytes"]
                )
                await session.execute(stmt)
                await session.commit()
            except Exception as e:
                logger.warning("Failed to persist server metrics: %s", e)
                # Don't raise, keep monitoring running