343 lines
11 KiB
Python
343 lines
11 KiB
Python
# -*- coding: utf-8 -*-
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"""
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日志配置模块
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统一的日志配置和管理，支持多 Worker 模式
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"""
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import logging
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import logging.handlers
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import sys
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import os
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import json
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from datetime import datetime, timezone
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from typing import Optional, Dict, Any
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from pathlib import Path
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from .config import settings
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class StructuredLogFormatter(logging.Formatter):
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"""结构化JSON日志格式化器
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将日志记录格式化为JSON格式，支持extra字段传递结构化数据。
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输出示例:
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{
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"timestamp": "2025-01-31T12:00:00Z",
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"level": "INFO",
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"logger": "app.services.asr",
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"message": "推理完成",
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"task_id": "xxx",
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"duration_ms": 1234,
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"audio_duration_sec": 60,
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"rtf": 0.02,
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"model_id": "qwen3-asr-1.7b"
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}
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"""
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def __init__(self, include_extra: bool = True):
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"""
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Args:
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include_extra: 是否包含extra字段中的结构化数据
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"""
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super().__init__()
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self.include_extra = include_extra
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self._reserved_attrs = {
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'name', 'msg', 'args', 'levelname', 'levelno', 'pathname',
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'filename', 'module', 'exc_info', 'exc_text', 'stack_info',
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'lineno', 'funcName', 'created', 'msecs', 'relativeCreated',
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'thread', 'threadName', 'processName', 'process', 'getMessage',
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'message', 'asctime'
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}
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def format(self, record: logging.LogRecord) -> str:
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"""将日志记录格式化为JSON"""
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log_data: Dict[str, Any] = {
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"timestamp": datetime.fromtimestamp(record.created, tz=timezone.utc).strftime("%Y-%m-%dT%H:%M:%S.%f")[:-3] + "Z",
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"level": record.levelname,
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"logger": record.name,
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"message": record.getMessage(),
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}
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# 添加worker_id（多worker模式下）
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workers = int(os.getenv("WORKERS", "1"))
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if workers > 1:
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log_data["worker_id"] = f"worker-{os.getpid()}"
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# 添加异常信息（如果有）
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if record.exc_info:
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exc_type = record.exc_info[0]
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exc_value = record.exc_info[1]
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if exc_type and exc_value:
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log_data["exception"] = {
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"type": exc_type.__name__,
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"message": str(exc_value)
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}
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# 添加extra字段中的结构化数据
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if self.include_extra:
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extra_data = self._extract_extra_data(record)
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if extra_data:
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log_data.update(extra_data)
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return json.dumps(log_data, ensure_ascii=False, default=str)
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def _extract_extra_data(self, record: logging.LogRecord) -> Dict[str, Any]:
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"""从日志记录中提取extra数据"""
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extra_data = {}
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for key, value in record.__dict__.items():
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if key not in self._reserved_attrs and not key.startswith('_'):
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extra_data[key] = value
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return extra_data
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class HybridLogFormatter(logging.Formatter):
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"""混合日志格式化器
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根据日志内容自动选择格式：
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- 包含结构化数据（extra字段）的日志使用JSON格式
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- 普通日志使用文本格式
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这允许在代码中逐步迁移到结构化日志，同时保持可读性。
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"""
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def __init__(
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self,
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text_format: Optional[str] = None,
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json_formatter: Optional[StructuredLogFormatter] = None,
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):
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super().__init__()
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self.text_format = text_format or "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
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self.json_formatter = json_formatter or StructuredLogFormatter()
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self._reserved_attrs = {
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'name', 'msg', 'args', 'levelname', 'levelno', 'pathname',
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'filename', 'module', 'exc_info', 'exc_text', 'stack_info',
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'lineno', 'funcName', 'created', 'msecs', 'relativeCreated',
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'thread', 'threadName', 'processName', 'process', 'getMessage',
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'message', 'asctime'
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}
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def format(self, record: logging.LogRecord) -> str:
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"""根据内容选择格式"""
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# 检查是否有extra数据
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has_extra = self._has_extra_data(record)
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if has_extra:
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return self.json_formatter.format(record)
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else:
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# 使用文本格式
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text_formatter = logging.Formatter(self.text_format)
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return text_formatter.format(record)
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def _has_extra_data(self, record: logging.LogRecord) -> bool:
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"""检查日志记录是否包含extra数据"""
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for key in record.__dict__.keys():
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if key not in self._reserved_attrs and not key.startswith('_'):
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return True
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return False
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def get_structured_logger(name: str) -> logging.Logger:
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"""获取支持结构化日志的记录器
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这是一个便捷函数，返回一个配置好的日志记录器，
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可以直接使用 extra 参数记录结构化数据。
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示例:
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logger = get_structured_logger(__name__)
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logger.info(
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"推理完成",
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extra={
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"duration_ms": 1234,
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"audio_duration_sec": 60,
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"rtf": 0.02,
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"model_id": "qwen3-asr-1.7b"
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}
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)
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Args:
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name: 记录器名称
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Returns:
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配置好的日志记录器
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"""
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return logging.getLogger(name)
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def log_inference_metrics(
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logger: logging.Logger,
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message: str,
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task_id: Optional[str] = None,
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duration_ms: Optional[float] = None,
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audio_duration_sec: Optional[float] = None,
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model_id: Optional[str] = None,
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status: str = "success",
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**kwargs
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) -> None:
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"""记录推理性能指标
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这是一个辅助函数，用于统一记录推理性能指标。
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Args:
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logger: 日志记录器
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message: 日志消息
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task_id: 任务ID
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duration_ms: 推理耗时（毫秒）
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audio_duration_sec: 音频时长（秒）
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model_id: 模型ID
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status: 状态（success/error）
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**kwargs: 其他结构化数据
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"""
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extra: Dict[str, Any] = {
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"status": status,
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}
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if task_id:
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extra["task_id"] = task_id
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if duration_ms is not None:
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extra["duration_ms"] = round(duration_ms, 2)
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if audio_duration_sec is not None:
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extra["audio_duration_sec"] = round(audio_duration_sec, 2)
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if model_id:
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extra["model_id"] = model_id
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# 计算RTF（实时率）
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if duration_ms is not None and audio_duration_sec is not None and audio_duration_sec > 0:
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rtf = (duration_ms / 1000) / audio_duration_sec
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extra["rtf"] = round(rtf, 4)
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# 添加其他数据
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extra.update(kwargs)
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logger.info(message, extra=extra)
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def get_worker_id() -> str:
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"""获取当前 Worker ID
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Returns:
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Worker 标识符，格式为 'worker-{pid}' 或 'main'
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"""
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# 检查是否在多 worker 模式下
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workers = int(os.getenv("WORKERS", "1"))
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if workers > 1:
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return f"worker-{os.getpid()}"
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return "main"
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def setup_logging(
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level: Optional[str] = None,
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log_file: Optional[str] = None,
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format_string: Optional[str] = None,
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max_bytes: Optional[int] = None,
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backup_count: Optional[int] = None,
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worker_id: Optional[str] = None,
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use_structured: bool = False,
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) -> None:
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"""设置应用日志配置
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Args:
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level: 日志级别
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log_file: 日志文件路径
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format_string: 日志格式字符串
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max_bytes: 单个日志文件最大大小（字节）
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backup_count: 保留的备份文件数量
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worker_id: Worker 标识符（多 Worker 模式下使用）
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use_structured: 是否使用结构化JSON日志格式
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"""
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# 使用传入的参数或配置文件中的设置
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log_level = level or settings.LOG_LEVEL
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log_file_path = log_file or settings.LOG_FILE
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max_file_size = max_bytes or settings.LOG_MAX_BYTES
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backup_files = backup_count or settings.LOG_BACKUP_COUNT
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# 获取 Worker ID
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current_worker_id = worker_id or get_worker_id()
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workers = int(os.getenv("WORKERS", "1"))
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worker_log_path: Optional[Path] = None
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# 确定日志格式
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if use_structured:
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# 使用结构化日志格式
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formatter: logging.Formatter = StructuredLogFormatter()
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else:
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# 使用混合格式（普通日志文本，带extra的JSON）
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if workers > 1:
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text_format = format_string or f"%(asctime)s - [{current_worker_id}] - %(name)s - %(levelname)s - %(message)s"
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else:
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text_format = format_string or "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
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formatter = HybridLogFormatter(text_format=text_format)
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# 创建处理器列表
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stream_handler = logging.StreamHandler(sys.stdout)
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stream_handler.setFormatter(formatter)
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handlers: list[logging.Handler] = [stream_handler]
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# 确定日志文件路径
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if log_file_path:
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log_path = Path(log_file_path)
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else:
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log_path = Path("logs/qwen3-asr.log")
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# 确保日志目录存在
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log_dir = log_path.parent
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log_dir.mkdir(parents=True, exist_ok=True)
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# 多 Worker 模式下，每个 Worker 使用独立的日志文件
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if workers > 1:
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# Example: qwen3-asr.log -> qwen3-asr.worker-12345.log
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worker_log_path = log_dir / f"{log_path.stem}.{current_worker_id}{log_path.suffix}"
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# Worker 专属日志文件
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worker_file_handler = logging.handlers.RotatingFileHandler(
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worker_log_path,
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maxBytes=max_file_size,
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backupCount=backup_files,
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encoding="utf-8",
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)
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worker_file_handler.setFormatter(formatter)
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handlers.append(worker_file_handler)
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# 同时也写入主日志文件（汇总所有 Worker 的日志）
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main_file_handler = logging.handlers.RotatingFileHandler(
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log_path,
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maxBytes=max_file_size,
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backupCount=backup_files,
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encoding="utf-8",
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)
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main_file_handler.setFormatter(formatter)
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handlers.append(main_file_handler)
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else:
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# 单 Worker 模式，只写入主日志文件
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file_handler = logging.handlers.RotatingFileHandler(
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log_path,
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maxBytes=max_file_size,
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backupCount=backup_files,
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encoding="utf-8",
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)
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file_handler.setFormatter(formatter)
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handlers.append(file_handler)
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# 配置根日志记录器
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logging.basicConfig(
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level=getattr(logging, log_level.upper()),
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handlers=handlers,
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force=True, # 强制重新配置
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)
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# 设置第三方库的日志级别（由LOG_LEVEL控制）
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third_party_level = getattr(logging, log_level.upper())
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logging.getLogger("urllib3").setLevel(third_party_level)
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logging.getLogger("requests").setLevel(third_party_level)
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logging.getLogger("httpx").setLevel(third_party_level)
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logging.getLogger("httpcore").setLevel(third_party_level)
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# 始终禁用噪音特别大的库
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logging.getLogger("numba").setLevel(logging.WARNING)
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logging.getLogger("numba.core").setLevel(logging.WARNING)
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logging.getLogger("numba.core.ssa").setLevel(logging.WARNING)
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# 多 Worker 模式下记录启动日志
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if workers > 1 and worker_log_path:
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logger = logging.getLogger(__name__)
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logger.info(f"Worker {current_worker_id} 日志系统已初始化，日志文件: {worker_log_path}")
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