mirror of
https://github.com/fawney19/Aether.git
synced 2026-09-02 09:20:22 +08:00
1d9c77522a
- 删除全部 Python 源码 (src/) 及 Alembic 迁移脚本，归档至 _deprecated_py_src/ - 重构 Rust gateway ai_pipeline: 拆分 planner/finalize 模块，新增 contracts/adaptation 层 - 重组 handlers 模块为 admin/public/proxy/internal/shared 子模块结构 - 新增 executor 模块，引入 Rust 原生数据库迁移 (aether-data/migrations) - 简化 CI/Docker 构建流程，移除 base image 二级构建，统一为单一 app image - 移除 Python 相关基础设施文件 (entrypoint.sh, gunicorn_conf.py, Dockerfile.base)
335 lines
12 KiB
Python
335 lines
12 KiB
Python
"""
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Prometheus监控插件
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支持将指标导出到Prometheus
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"""
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from __future__ import annotations
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import asyncio
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from typing import Any
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try:
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from prometheus_client import REGISTRY, Counter, Gauge, Histogram, Summary, generate_latest
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PROMETHEUS_AVAILABLE = True
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except ImportError:
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# Prometheus client not installed, plugin will be disabled
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PROMETHEUS_AVAILABLE = False
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Counter = Gauge = Histogram = Summary = REGISTRY = generate_latest = None
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from src.core.logger import logger
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from .base import Metric, MetricType, MonitorPlugin
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class PrometheusPlugin(MonitorPlugin):
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"""
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Prometheus监控插件
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使用prometheus_client库导出指标
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"""
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def __init__(self, name: str = "prometheus", config: dict[str, Any] | None = None):
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super().__init__(name, config)
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# Check if prometheus_client is available
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if not PROMETHEUS_AVAILABLE:
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self.enabled = False
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logger.warning("Prometheus client not installed, plugin disabled")
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return
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# 指标注册表
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self._metrics: dict[str, Any] = {}
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self._max_dynamic_metrics: int = 100 # 动态创建的指标数上限
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self._buffer: list[Metric] = []
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self._lock = asyncio.Lock()
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self._flush_task: asyncio.Task | None = None # 跟踪后台任务
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# 预定义常用指标
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self._init_default_metrics()
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# 启动刷新任务
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self._start_flush_task()
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def _init_default_metrics(self) -> None:
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"""初始化默认指标"""
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# HTTP请求指标
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http_label_names = ["method", "endpoint", "status", "status_class"]
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self._metrics["http_requests_total"] = Counter(
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"http_requests_total",
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"Total HTTP requests",
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http_label_names,
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)
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self._metrics["http_request_duration_seconds"] = Histogram(
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"http_request_duration_seconds",
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"HTTP request duration in seconds",
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http_label_names,
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buckets=(0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1, 2.5, 5, 10),
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)
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self._metrics["http_errors_total"] = Counter(
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"http_errors_total",
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"Total HTTP errors",
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http_label_names,
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)
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# Token使用指标（仅按 provider 维度聚合，避免 provider x model 笛卡尔积导致基数爆炸）
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self._metrics["tokens_input_total"] = Counter(
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"tokens_input_total", "Total input tokens", ["provider"]
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)
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self._metrics["tokens_output_total"] = Counter(
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"tokens_output_total", "Total output tokens", ["provider"]
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)
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self._metrics["tokens_total"] = Counter("tokens_total", "Total tokens", ["provider"])
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self._metrics["usage_cost_total"] = Counter(
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"usage_cost_total", "Total usage cost in USD", ["provider"]
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)
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# 系统指标
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self._metrics["active_connections"] = Gauge(
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"active_connections", "Number of active connections"
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)
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self._metrics["cache_hits_total"] = Counter(
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"cache_hits_total", "Total cache hits", ["cache_type"]
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)
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self._metrics["cache_misses_total"] = Counter(
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"cache_misses_total", "Total cache misses", ["cache_type"]
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)
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# 提供商健康指标
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self._metrics["provider_health"] = Gauge(
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"provider_health", "Provider health status (1=healthy, 0=unhealthy)", ["provider"]
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)
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self._metrics["provider_latency_seconds"] = Histogram(
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"provider_latency_seconds",
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"Provider response latency in seconds",
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["provider"],
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buckets=(0.1, 0.25, 0.5, 1, 2.5, 5, 10, 30),
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)
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def _start_flush_task(self) -> None:
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"""启动定期刷新任务"""
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async def flush_loop() -> Any:
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try:
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while self.enabled:
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await asyncio.sleep(self.flush_interval)
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if self.enabled: # 再次检查，避免关闭时执行
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await self.flush()
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except asyncio.CancelledError:
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# 任务被取消，正常关闭
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logger.debug("Prometheus flush task cancelled")
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except Exception as e:
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logger.error(f"Prometheus flush loop error: {e}")
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# 保存任务句柄以便后续取消
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try:
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loop = asyncio.get_running_loop()
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self._flush_task = loop.create_task(flush_loop())
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except RuntimeError:
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# 如果没有运行的事件循环，任务将在后续创建
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logger.warning("No event loop available for Prometheus flush task")
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def _get_or_create_metric(
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self, name: str, metric_type: MetricType, labels: list[str] | None = None
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) -> Any | None:
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"""获取或创建指标，超过上限后拒绝创建新指标"""
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if name not in self._metrics:
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if len(self._metrics) >= self._max_dynamic_metrics:
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logger.warning("Prometheus dynamic metrics limit reached, dropping: {}", name)
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return None
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labels = labels or []
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if metric_type == MetricType.COUNTER:
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self._metrics[name] = Counter(name, f"Auto-created counter {name}", labels)
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elif metric_type == MetricType.GAUGE:
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self._metrics[name] = Gauge(name, f"Auto-created gauge {name}", labels)
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elif metric_type == MetricType.HISTOGRAM:
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self._metrics[name] = Histogram(name, f"Auto-created histogram {name}", labels)
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elif metric_type == MetricType.SUMMARY:
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self._metrics[name] = Summary(name, f"Auto-created summary {name}", labels)
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return self._metrics.get(name)
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async def record_metric(self, metric: Metric) -> None:
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"""记录单个指标"""
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async with self._lock:
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self._buffer.append(metric)
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# 如果缓冲区满，自动刷新
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if len(self._buffer) >= self.batch_size:
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await self.flush()
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async def record_batch(self, metrics: list[Metric]) -> None:
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"""批量记录指标"""
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async with self._lock:
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self._buffer.extend(metrics)
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# 如果缓冲区满，自动刷新
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if len(self._buffer) >= self.batch_size:
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await self.flush()
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async def increment(
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self, name: str, value: float = 1, labels: dict[str, str] | None = None
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) -> Any:
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"""增加计数器"""
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try:
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if name in self._metrics:
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metric = self._metrics[name]
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if labels:
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# 过滤掉不存在的标签
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filtered_labels = {k: v for k, v in labels.items() if k in metric._labelnames}
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metric.labels(**filtered_labels).inc(value)
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else:
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metric.inc(value)
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else:
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# 创建新的计数器
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label_names = list(labels.keys()) if labels else []
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metric = self._get_or_create_metric(name, MetricType.COUNTER, label_names)
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if metric is None:
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return
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if labels:
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metric.labels(**labels).inc(value)
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else:
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metric.inc(value)
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except Exception as e:
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# 记录错误但不中断
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logger.warning(f"Error recording metric {name}: {e}")
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async def gauge(self, name: str, value: float, labels: dict[str, str] | None = None) -> Any:
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"""设置仪表值"""
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try:
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if name in self._metrics:
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metric = self._metrics[name]
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if labels:
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filtered_labels = {k: v for k, v in labels.items() if k in metric._labelnames}
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metric.labels(**filtered_labels).set(value)
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else:
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metric.set(value)
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else:
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# 创建新的仪表
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label_names = list(labels.keys()) if labels else []
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metric = self._get_or_create_metric(name, MetricType.GAUGE, label_names)
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if metric is None:
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return
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if labels:
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metric.labels(**labels).set(value)
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else:
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metric.set(value)
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except Exception as e:
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logger.warning(f"Error recording gauge {name}: {e}")
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async def histogram(
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self,
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name: str,
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value: float,
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labels: dict[str, str] | None = None,
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buckets: list[float] | None = None,
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) -> Any:
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"""记录直方图数据"""
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try:
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if name in self._metrics:
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metric = self._metrics[name]
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if labels:
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filtered_labels = {k: v for k, v in labels.items() if k in metric._labelnames}
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metric.labels(**filtered_labels).observe(value)
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else:
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metric.observe(value)
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else:
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# 创建新的直方图
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label_names = list(labels.keys()) if labels else []
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if buckets:
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if len(self._metrics) >= self._max_dynamic_metrics:
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return
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metric = Histogram(
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name, f"Auto-created histogram {name}", label_names, buckets=buckets
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)
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else:
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metric = self._get_or_create_metric(name, MetricType.HISTOGRAM, label_names)
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if metric is None:
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return
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self._metrics[name] = metric
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if labels:
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metric.labels(**labels).observe(value)
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else:
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metric.observe(value)
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except Exception as e:
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logger.warning(f"Error recording histogram {name}: {e}")
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async def timing(self, name: str, duration: float, labels: dict[str, str] | None = None) -> Any:
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"""记录时间指标"""
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# 使用直方图记录时间
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await self.histogram(f"{name}_seconds", duration, labels)
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async def flush(self) -> Any:
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"""刷新缓冲的指标到Prometheus"""
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async with self._lock:
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if not self._buffer:
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return
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# 处理缓冲区中的指标
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for metric in self._buffer:
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if metric.metric_type == MetricType.COUNTER:
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await self.increment(metric.name, metric.value, metric.labels)
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elif metric.metric_type == MetricType.GAUGE:
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await self.gauge(metric.name, metric.value, metric.labels)
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elif metric.metric_type == MetricType.HISTOGRAM:
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await self.histogram(metric.name, metric.value, metric.labels)
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# 清空缓冲区
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self._buffer.clear()
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async def get_stats(self) -> dict[str, Any]:
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"""获取插件统计信息"""
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return {
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"type": "prometheus",
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"metrics_count": len(self._metrics),
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"buffer_size": len(self._buffer),
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"flush_interval": self.flush_interval,
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"batch_size": self.batch_size,
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}
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def get_metrics(self) -> bytes:
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"""
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获取Prometheus格式的指标数据
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Returns:
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Prometheus文本格式的指标
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"""
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return generate_latest(REGISTRY)
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async def shutdown(self) -> Any:
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"""
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关闭插件，取消后台任务
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这个方法应该在应用关闭时调用
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"""
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# 禁用插件
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self.enabled = False
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# 取消并等待后台任务完成
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if self._flush_task and not self._flush_task.done():
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self._flush_task.cancel()
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try:
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await self._flush_task
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except asyncio.CancelledError:
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pass
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# 最后一次刷新缓冲区
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await self.flush()
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logger.info("Prometheus plugin shutdown complete")
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async def cleanup(self) -> Any:
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"""
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清理资源（别名方法）
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"""
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await self.shutdown()
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