完整的 Python 日志记录指南：最佳实践和实施

为什么正确的日志记录很重要

在深入研究技术细节之前，让我们了解为什么正确的日志记录很重要：

• 在生产中实现有效调试
• 提供对应用程序行为的见解
• 促进性能监控
• 帮助跟踪安全事件
• 支持合规性要求
• 提高维护效率

Python 日志记录快速入门

对于那些刚接触 Python 日志记录的人来说，这里有一个使用 logging.basicConfig:
的基本示例

# Simple python logging example
import logging

# Basic logger in python example
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# Create a logger
logger = logging.getLogger(__name__)

# Logger in python example
logger.info("This is an information message")
logger.warning("This is a warning message")

此示例演示了 python 中日志记录模块的基础知识，并展示了如何在应用程序中使用 python 记录器日志记录。

Python 日志模块入门

基本设置

让我们从简单的日志配置开始：

import logging

# Basic configuration
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# Your first logger
logger = logging.getLogger(__name__)

# Using the logger
logger.info("Application started")
logger.warning("Watch out!")
logger.error("Something went wrong")

了解日志级别

Python 日志记录有五个标准级别：

Level	Numeric Value	When to Use
DEBUG	10	Detailed information for diagnosing problems
INFO	20	General operational events
WARNING	30	Something unexpected happened
ERROR	40	More serious problem
CRITICAL	50	Program may not be able to continue

超越 print() 语句

为什么选择记录而不是打印语句？

• 严重级别以便更好地分类
• 时间戳信息
• 来源信息（文件、行号）
• 可配置的输出目的地
• 生产就绪过滤
• 线程安全

配置您的日志系统

基本配置选项

# Simple python logging example
import logging

# Basic logger in python example
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# Create a logger
logger = logging.getLogger(__name__)

# Logger in python example
logger.info("This is an information message")
logger.warning("This is a warning message")

高级配置

对于更复杂的应用：

import logging

# Basic configuration
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# Your first logger
logger = logging.getLogger(__name__)

# Using the logger
logger.info("Application started")
logger.warning("Watch out!")
logger.error("Something went wrong")

使用高级日志记录

结构化日志记录

结构化日志记录提供了一致的、机器可读的格式，这对于日志分析和监控至关重要。有关结构化日志记录模式和最佳实践的全面概述，请查看结构化日志记录指南。让我们用 Python 实现结构化日志记录：

logging.basicConfig(
    filename='app.log',
    filemode='w',
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    level=logging.DEBUG,
    datefmt='%Y-%m-%d %H:%M:%S'
)

错误管理

正确的错误记录对于调试生产问题至关重要。这是一个全面的方法：

config = {
    'version': 1,
    'formatters': {
        'detailed': {
            'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        }
    },
    'handlers': {
        'console': {
            'class': 'logging.StreamHandler',
            'level': 'INFO',
            'formatter': 'detailed'
        },
        'file': {
            'class': 'logging.FileHandler',
            'filename': 'app.log',
            'level': 'DEBUG',
            'formatter': 'detailed'
        }
    },
    'loggers': {
        'myapp': {
            'handlers': ['console', 'file'],
            'level': 'DEBUG',
            'propagate': True
        }
    }
}

logging.config.dictConfig(config)

并发日志记录

登录多线程应用时，需要保证线程安全：

import json
import logging
from datetime import datetime

class JSONFormatter(logging.Formatter):
    def __init__(self):
        super().__init__()

    def format(self, record):
        # Create base log record
        log_obj = {
            "timestamp": self.formatTime(record, self.datefmt),
            "name": record.name,
            "level": record.levelname,
            "message": record.getMessage(),
            "module": record.module,
            "function": record.funcName,
            "line": record.lineno
        }

        # Add exception info if present
        if record.exc_info:
            log_obj["exception"] = self.formatException(record.exc_info)

        # Add custom fields from extra
        if hasattr(record, "extra_fields"):
            log_obj.update(record.extra_fields)

        return json.dumps(log_obj)

# Usage Example
logger = logging.getLogger(__name__)
handler = logging.StreamHandler()
handler.setFormatter(JSONFormatter())
logger.addHandler(handler)

# Log with extra fields
logger.info("User logged in", extra={"extra_fields": {"user_id": "123", "ip": "192.168.1.1"}})

不同环境下的登录

不同的应用程序环境需要特定的日志记录方法。无论您使用的是 Web 应用程序、微服务还是后台任务，每个环境都有独特的日志记录要求和最佳实践。让我们探讨如何在各种部署场景中实现有效的日志记录。

Web 应用程序日志记录

Django 日志配置

这是一个全面的 Django 日志记录设置：

import traceback
import sys
from contextlib import contextmanager

class ErrorLogger:
    def __init__(self, logger):
        self.logger = logger

    @contextmanager
    def error_context(self, operation_name, **context):
        """Context manager for error logging with additional context"""
        try:
            yield
        except Exception as e:
            # Capture the current stack trace
            exc_type, exc_value, exc_traceback = sys.exc_info()

            # Format error details
            error_details = {
                "operation": operation_name,
                "error_type": exc_type.__name__,
                "error_message": str(exc_value),
                "context": context,
                "stack_trace": traceback.format_exception(exc_type, exc_value, exc_traceback)
            }

            # Log the error with full context
            self.logger.error(
                f"Error in {operation_name}: {str(exc_value)}",
                extra={"error_details": error_details}
            )

            # Re-raise the exception
            raise

# Usage Example
logger = logging.getLogger(__name__)
error_logger = ErrorLogger(logger)

with error_logger.error_context("user_authentication", user_id="123", attempt=2):
    # Your code that might raise an exception
    authenticate_user(user_id)

Flask 日志记录设置

Flask 提供了自己的可以定制的日志系统：

import threading
import logging
from queue import Queue
from logging.handlers import QueueHandler, QueueListener

def setup_thread_safe_logging():
    """Set up thread-safe logging with a queue"""
    # Create the queue
    log_queue = Queue()

    # Create handlers
    console_handler = logging.StreamHandler()
    file_handler = logging.FileHandler('app.log')

    # Create queue handler and listener
    queue_handler = QueueHandler(log_queue)
    listener = QueueListener(
        log_queue,
        console_handler,
        file_handler,
        respect_handler_level=True
    )

    # Configure root logger
    root_logger = logging.getLogger()
    root_logger.addHandler(queue_handler)

    # Start the listener in a separate thread
    listener.start()

    return listener

# Usage
listener = setup_thread_safe_logging()

def worker_function():
    logger = logging.getLogger(__name__)
    logger.info(f"Worker thread {threading.current_thread().name} starting")
    # Do work...
    logger.info(f"Worker thread {threading.current_thread().name} finished")

# Create and start threads
threads = [
    threading.Thread(target=worker_function)
    for _ in range(3)
]
for thread in threads:
    thread.start()

FastAPI 日志记录实践

FastAPI 可以利用 Python 的日志记录和一些中间件增强功能：

# settings.py
LOGGING = {
    'version': 1,
    'disable_existing_loggers': False,
    'formatters': {
        'verbose': {
            'format': '{levelname} {asctime} {module} {process:d} {thread:d} {message}',
            'style': '{',
        },
        'simple': {
            'format': '{levelname} {message}',
            'style': '{',
        },
    },
    'filters': {
        'require_debug_true': {
            '()': 'django.utils.log.RequireDebugTrue',
        },
    },
    'handlers': {
        'console': {
            'level': 'INFO',
            'filters': ['require_debug_true'],
            'class': 'logging.StreamHandler',
            'formatter': 'simple'
        },
        'file': {
            'level': 'ERROR',
            'class': 'logging.FileHandler',
            'filename': 'django-errors.log',
            'formatter': 'verbose'
        },
        'mail_admins': {
            'level': 'ERROR',
            'class': 'django.utils.log.AdminEmailHandler',
            'include_html': True,
        }
    },
    'loggers': {
        'django': {
            'handlers': ['console'],
            'propagate': True,
        },
        'django.request': {
            'handlers': ['file', 'mail_admins'],
            'level': 'ERROR',
            'propagate': False,
        },
        'myapp': {
            'handlers': ['console', 'file'],
            'level': 'INFO',
        }
    }
}

微服务日志记录

对于微服务，分布式跟踪和关联 ID 至关重要：

import logging
from logging.handlers import RotatingFileHandler
from flask import Flask, request

app = Flask(__name__)

def setup_logger():
    # Create formatter
    formatter = logging.Formatter(
        '[%(asctime)s] %(levelname)s in %(module)s: %(message)s'
    )

    # File Handler
    file_handler = RotatingFileHandler(
        'flask_app.log',
        maxBytes=10485760,  # 10MB
        backupCount=10
    )
    file_handler.setLevel(logging.INFO)
    file_handler.setFormatter(formatter)

    # Add request context
    class RequestFormatter(logging.Formatter):
        def format(self, record):
            record.url = request.url
            record.remote_addr = request.remote_addr
            return super().format(record)

    # Configure app logger
    app.logger.addHandler(file_handler)
    app.logger.setLevel(logging.INFO)

    return app.logger

# Usage in routes
@app.route('/api/endpoint')
def api_endpoint():
    app.logger.info(f'Request received from {request.remote_addr}')
    # Your code here
    return jsonify({'status': 'success'})

后台任务记录

对于后台任务，我们需要确保正确的日志处理和轮换：

from fastapi import FastAPI, Request
from typing import Callable
import logging
import time

app = FastAPI()

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# Middleware for request logging
@app.middleware("http")
async def log_requests(request: Request, call_next: Callable):
    start_time = time.time()
    response = await call_next(request)
    duration = time.time() - start_time

    log_dict = {
        "url": str(request.url),
        "method": request.method,
        "client_ip": request.client.host,
        "duration": f"{duration:.2f}s",
        "status_code": response.status_code
    }

    logger.info(f"Request processed: {log_dict}")
    return response

# Example endpoint with logging
@app.get("/items/{item_id}")
async def read_item(item_id: int):
    logger.info(f"Retrieving item {item_id}")
    # Your code here
    return {"item_id": item_id}

常见的日志记录模式和解决方案

请求 ID 跟踪

在您的应用程序中实施请求跟踪：

import logging
import contextvars
from uuid import uuid4

# Create context variable for trace ID
trace_id_var = contextvars.ContextVar('trace_id', default=None)

class TraceIDFilter(logging.Filter):
    def filter(self, record):
        trace_id = trace_id_var.get()
        record.trace_id = trace_id if trace_id else 'no_trace'
        return True

def setup_microservice_logging(service_name):
    logger = logging.getLogger(service_name)

    # Create formatter with trace ID
    formatter = logging.Formatter(
        '%(asctime)s - %(name)s - [%(trace_id)s] - %(levelname)s - %(message)s'
    )

    # Add handlers with trace ID filter
    handler = logging.StreamHandler()
    handler.setFormatter(formatter)
    handler.addFilter(TraceIDFilter())

    logger.addHandler(handler)
    logger.setLevel(logging.INFO)

    return logger

# Usage in microservice
logger = setup_microservice_logging('order_service')

def process_order(order_data):
    # Generate or get trace ID from request
    trace_id_var.set(str(uuid4()))

    logger.info("Starting order processing", extra={
        'order_id': order_data['id'],
        'customer_id': order_data['customer_id']
    })

    # Process order...

    logger.info("Order processed successfully")

用户活动记录

安全地跟踪用户操作：

# Simple python logging example
import logging

# Basic logger in python example
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# Create a logger
logger = logging.getLogger(__name__)

# Logger in python example
logger.info("This is an information message")
logger.warning("This is a warning message")

故障排除和调试

有效地排除日志记录问题需要了解常见问题及其解决方案。本节涵盖开发人员在实现日志记录时面临的最常见挑战，并提供调试日志记录配置的实用解决方案。

常见日志记录问题

丢失日志条目

import logging

# Basic configuration
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# Your first logger
logger = logging.getLogger(__name__)

# Using the logger
logger.info("Application started")
logger.warning("Watch out!")
logger.error("Something went wrong")

性能瓶颈

logging.basicConfig(
    filename='app.log',
    filemode='w',
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    level=logging.DEBUG,
    datefmt='%Y-%m-%d %H:%M:%S'
)

常见的日志记录陷阱和解决方案

配置问题

config = {
    'version': 1,
    'formatters': {
        'detailed': {
            'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        }
    },
    'handlers': {
        'console': {
            'class': 'logging.StreamHandler',
            'level': 'INFO',
            'formatter': 'detailed'
        },
        'file': {
            'class': 'logging.FileHandler',
            'filename': 'app.log',
            'level': 'DEBUG',
            'formatter': 'detailed'
        }
    },
    'loggers': {
        'myapp': {
            'handlers': ['console', 'file'],
            'level': 'DEBUG',
            'propagate': True
        }
    }
}

logging.config.dictConfig(config)

内存和资源问题

import json
import logging
from datetime import datetime

class JSONFormatter(logging.Formatter):
    def __init__(self):
        super().__init__()

    def format(self, record):
        # Create base log record
        log_obj = {
            "timestamp": self.formatTime(record, self.datefmt),
            "name": record.name,
            "level": record.levelname,
            "message": record.getMessage(),
            "module": record.module,
            "function": record.funcName,
            "line": record.lineno
        }

        # Add exception info if present
        if record.exc_info:
            log_obj["exception"] = self.formatException(record.exc_info)

        # Add custom fields from extra
        if hasattr(record, "extra_fields"):
            log_obj.update(record.extra_fields)

        return json.dumps(log_obj)

# Usage Example
logger = logging.getLogger(__name__)
handler = logging.StreamHandler()
handler.setFormatter(JSONFormatter())
logger.addHandler(handler)

# Log with extra fields
logger.info("User logged in", extra={"extra_fields": {"user_id": "123", "ip": "192.168.1.1"}})

格式字符串和性能问题

import traceback
import sys
from contextlib import contextmanager

class ErrorLogger:
    def __init__(self, logger):
        self.logger = logger

    @contextmanager
    def error_context(self, operation_name, **context):
        """Context manager for error logging with additional context"""
        try:
            yield
        except Exception as e:
            # Capture the current stack trace
            exc_type, exc_value, exc_traceback = sys.exc_info()

            # Format error details
            error_details = {
                "operation": operation_name,
                "error_type": exc_type.__name__,
                "error_message": str(exc_value),
                "context": context,
                "stack_trace": traceback.format_exception(exc_type, exc_value, exc_traceback)
            }

            # Log the error with full context
            self.logger.error(
                f"Error in {operation_name}: {str(exc_value)}",
                extra={"error_details": error_details}
            )

            # Re-raise the exception
            raise

# Usage Example
logger = logging.getLogger(__name__)
error_logger = ErrorLogger(logger)

with error_logger.error_context("user_authentication", user_id="123", attempt=2):
    # Your code that might raise an exception
    authenticate_user(user_id)

处理程序配置陷阱

import threading
import logging
from queue import Queue
from logging.handlers import QueueHandler, QueueListener

def setup_thread_safe_logging():
    """Set up thread-safe logging with a queue"""
    # Create the queue
    log_queue = Queue()

    # Create handlers
    console_handler = logging.StreamHandler()
    file_handler = logging.FileHandler('app.log')

    # Create queue handler and listener
    queue_handler = QueueHandler(log_queue)
    listener = QueueListener(
        log_queue,
        console_handler,
        file_handler,
        respect_handler_level=True
    )

    # Configure root logger
    root_logger = logging.getLogger()
    root_logger.addHandler(queue_handler)

    # Start the listener in a separate thread
    listener.start()

    return listener

# Usage
listener = setup_thread_safe_logging()

def worker_function():
    logger = logging.getLogger(__name__)
    logger.info(f"Worker thread {threading.current_thread().name} starting")
    # Do work...
    logger.info(f"Worker thread {threading.current_thread().name} finished")

# Create and start threads
threads = [
    threading.Thread(target=worker_function)
    for _ in range(3)
]
for thread in threads:
    thread.start()

线程安全注意事项

# settings.py
LOGGING = {
    'version': 1,
    'disable_existing_loggers': False,
    'formatters': {
        'verbose': {
            'format': '{levelname} {asctime} {module} {process:d} {thread:d} {message}',
            'style': '{',
        },
        'simple': {
            'format': '{levelname} {message}',
            'style': '{',
        },
    },
    'filters': {
        'require_debug_true': {
            '()': 'django.utils.log.RequireDebugTrue',
        },
    },
    'handlers': {
        'console': {
            'level': 'INFO',
            'filters': ['require_debug_true'],
            'class': 'logging.StreamHandler',
            'formatter': 'simple'
        },
        'file': {
            'level': 'ERROR',
            'class': 'logging.FileHandler',
            'filename': 'django-errors.log',
            'formatter': 'verbose'
        },
        'mail_admins': {
            'level': 'ERROR',
            'class': 'django.utils.log.AdminEmailHandler',
            'include_html': True,
        }
    },
    'loggers': {
        'django': {
            'handlers': ['console'],
            'propagate': True,
        },
        'django.request': {
            'handlers': ['file', 'mail_admins'],
            'level': 'ERROR',
            'propagate': False,
        },
        'myapp': {
            'handlers': ['console', 'file'],
            'level': 'INFO',
        }
    }
}

配置文件问题

import logging
from logging.handlers import RotatingFileHandler
from flask import Flask, request

app = Flask(__name__)

def setup_logger():
    # Create formatter
    formatter = logging.Formatter(
        '[%(asctime)s] %(levelname)s in %(module)s: %(message)s'
    )

    # File Handler
    file_handler = RotatingFileHandler(
        'flask_app.log',
        maxBytes=10485760,  # 10MB
        backupCount=10
    )
    file_handler.setLevel(logging.INFO)
    file_handler.setFormatter(formatter)

    # Add request context
    class RequestFormatter(logging.Formatter):
        def format(self, record):
            record.url = request.url
            record.remote_addr = request.remote_addr
            return super().format(record)

    # Configure app logger
    app.logger.addHandler(file_handler)
    app.logger.setLevel(logging.INFO)

    return app.logger

# Usage in routes
@app.route('/api/endpoint')
def api_endpoint():
    app.logger.info(f'Request received from {request.remote_addr}')
    # Your code here
    return jsonify({'status': 'success'})

测试您的日志记录

使用日志进行单元测试

from fastapi import FastAPI, Request
from typing import Callable
import logging
import time

app = FastAPI()

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# Middleware for request logging
@app.middleware("http")
async def log_requests(request: Request, call_next: Callable):
    start_time = time.time()
    response = await call_next(request)
    duration = time.time() - start_time

    log_dict = {
        "url": str(request.url),
        "method": request.method,
        "client_ip": request.client.host,
        "duration": f"{duration:.2f}s",
        "status_code": response.status_code
    }

    logger.info(f"Request processed: {log_dict}")
    return response

# Example endpoint with logging
@app.get("/items/{item_id}")
async def read_item(item_id: int):
    logger.info(f"Retrieving item {item_id}")
    # Your code here
    return {"item_id": item_id}

使用模拟记录器进行测试

import logging
import contextvars
from uuid import uuid4

# Create context variable for trace ID
trace_id_var = contextvars.ContextVar('trace_id', default=None)

class TraceIDFilter(logging.Filter):
    def filter(self, record):
        trace_id = trace_id_var.get()
        record.trace_id = trace_id if trace_id else 'no_trace'
        return True

def setup_microservice_logging(service_name):
    logger = logging.getLogger(service_name)

    # Create formatter with trace ID
    formatter = logging.Formatter(
        '%(asctime)s - %(name)s - [%(trace_id)s] - %(levelname)s - %(message)s'
    )

    # Add handlers with trace ID filter
    handler = logging.StreamHandler()
    handler.setFormatter(formatter)
    handler.addFilter(TraceIDFilter())

    logger.addHandler(handler)
    logger.setLevel(logging.INFO)

    return logger

# Usage in microservice
logger = setup_microservice_logging('order_service')

def process_order(order_data):
    # Generate or get trace ID from request
    trace_id_var.set(str(uuid4()))

    logger.info("Starting order processing", extra={
        'order_id': order_data['id'],
        'customer_id': order_data['customer_id']
    })

    # Process order...

    logger.info("Order processed successfully")

替代记录解决方案

洛古鲁

Loguru 提供了一个更简单的日志记录界面，具有开箱即用的强大功能：

from logging.handlers import RotatingFileHandler
import logging
import threading
from datetime import datetime

class BackgroundTaskLogger:
    def __init__(self, task_name):
        self.logger = logging.getLogger(f'background_task.{task_name}')
        self.setup_logging()

    def setup_logging(self):
        # Create logs directory if it doesn't exist
        import os
        os.makedirs('logs', exist_ok=True)

        # Setup rotating file handler
        handler = RotatingFileHandler(
            filename=f'logs/task_{datetime.now():%Y%m%d}.log',
            maxBytes=5*1024*1024,  # 5MB
            backupCount=5
        )

        # Create formatter
        formatter = logging.Formatter(
            '%(asctime)s - [%(threadName)s] - %(levelname)s - %(message)s'
        )
        handler.setFormatter(formatter)

        self.logger.addHandler(handler)
        self.logger.setLevel(logging.INFO)

    def log_task_status(self, status, **kwargs):
        """Log task status with additional context"""
        extra = {
            'thread_id': threading.get_ident(),
            'timestamp': datetime.now().isoformat(),
            **kwargs
        }
        self.logger.info(f"Task status: {status}", extra=extra)

# Usage example
def background_job():
    logger = BackgroundTaskLogger('data_processing')
    try:
        logger.log_task_status('started', job_id=123)
        # Do some work...
        logger.log_task_status('completed', records_processed=1000)
    except Exception as e:
        logger.logger.error(f"Task failed: {str(e)}", exc_info=True)

结构日志

Structlog 非常适合使用上下文进行结构化日志记录：

import logging
from contextlib import contextmanager
import threading
import uuid

# Store request ID in thread-local storage
_request_id = threading.local()

class RequestIDFilter(logging.Filter):
    def filter(self, record):
        record.request_id = getattr(_request_id, 'id', 'no_request_id')
        return True

@contextmanager
def request_context(request_id=None):
    """Context manager for request tracking"""
    if request_id is None:
        request_id = str(uuid.uuid4())

    old_id = getattr(_request_id, 'id', None)
    _request_id.id = request_id
    try:
        yield request_id
    finally:
        if old_id is None:
            del _request_id.id
        else:
            _request_id.id = old_id

# Setup logging with request ID
def setup_request_logging():
    logger = logging.getLogger()
    formatter = logging.Formatter(
        '%(asctime)s - [%(request_id)s] - %(levelname)s - %(message)s'
    )

    handler = logging.StreamHandler()
    handler.setFormatter(formatter)
    handler.addFilter(RequestIDFilter())

    logger.addHandler(handler)
    return logger

# Usage example
logger = setup_request_logging()

def process_request(data):
    with request_context() as request_id:
        logger.info("Processing request", extra={
            'data': data,
            'operation': 'process_request'
        })
        # Process the request...
        logger.info("Request processed successfully")

Python-JSON-记录器

对于 JSON 格式的日志记录：

# Simple python logging example
import logging

# Basic logger in python example
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# Create a logger
logger = logging.getLogger(__name__)

# Logger in python example
logger.info("This is an information message")
logger.warning("This is a warning message")

最佳实践和指南

测井标准

import logging

# Basic configuration
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)

# Your first logger
logger = logging.getLogger(__name__)

# Using the logger
logger.info("Application started")
logger.warning("Watch out!")
logger.error("Something went wrong")

性能优化

logging.basicConfig(
    filename='app.log',
    filemode='w',
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    level=logging.DEBUG,
    datefmt='%Y-%m-%d %H:%M:%S'
)

案例研究

现实世界的实施：电子商务平台

config = {
    'version': 1,
    'formatters': {
        'detailed': {
            'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        }
    },
    'handlers': {
        'console': {
            'class': 'logging.StreamHandler',
            'level': 'INFO',
            'formatter': 'detailed'
        },
        'file': {
            'class': 'logging.FileHandler',
            'filename': 'app.log',
            'level': 'DEBUG',
            'formatter': 'detailed'
        }
    },
    'loggers': {
        'myapp': {
            'handlers': ['console', 'file'],
            'level': 'DEBUG',
            'propagate': True
        }
    }
}

logging.config.dictConfig(config)

微服务架构示例

import json
import logging
from datetime import datetime

class JSONFormatter(logging.Formatter):
    def __init__(self):
        super().__init__()

    def format(self, record):
        # Create base log record
        log_obj = {
            "timestamp": self.formatTime(record, self.datefmt),
            "name": record.name,
            "level": record.levelname,
            "message": record.getMessage(),
            "module": record.module,
            "function": record.funcName,
            "line": record.lineno
        }

        # Add exception info if present
        if record.exc_info:
            log_obj["exception"] = self.formatException(record.exc_info)

        # Add custom fields from extra
        if hasattr(record, "extra_fields"):
            log_obj.update(record.extra_fields)

        return json.dumps(log_obj)

# Usage Example
logger = logging.getLogger(__name__)
handler = logging.StreamHandler()
handler.setFormatter(JSONFormatter())
logger.addHandler(handler)

# Log with extra fields
logger.info("User logged in", extra={"extra_fields": {"user_id": "123", "ip": "192.168.1.1"}})

结论

要点

1. 基础优先：从正确的基本配置开始

• 设置适当的日志级别
• 配置有意义的格式
• 选择合适的处理程序

1. 结构化方法：使用结构化日志记录进行更好的分析

• 一致的日志格式
• 上下文信息
• 机器可解析的输出

1. 性能很重要：优化生产日志记录

• 实施日志轮转
• 需要时使用异步日志记录
• 考虑抽样策略

1. 安全意识：保护敏感信息
   • 过滤敏感数据
   • 实施适当的访问控制
   • 遵守合规要求

实施清单

import traceback
import sys
from contextlib import contextmanager

class ErrorLogger:
    def __init__(self, logger):
        self.logger = logger

    @contextmanager
    def error_context(self, operation_name, **context):
        """Context manager for error logging with additional context"""
        try:
            yield
        except Exception as e:
            # Capture the current stack trace
            exc_type, exc_value, exc_traceback = sys.exc_info()

            # Format error details
            error_details = {
                "operation": operation_name,
                "error_type": exc_type.__name__,
                "error_message": str(exc_value),
                "context": context,
                "stack_trace": traceback.format_exception(exc_type, exc_value, exc_traceback)
            }

            # Log the error with full context
            self.logger.error(
                f"Error in {operation_name}: {str(exc_value)}",
                extra={"error_details": error_details}
            )

            # Re-raise the exception
            raise

# Usage Example
logger = logging.getLogger(__name__)
error_logger = ErrorLogger(logger)

with error_logger.error_context("user_authentication", user_id="123", attempt=2):
    # Your code that might raise an exception
    authenticate_user(user_id)

其他资源

1. 官方文档：

• Python 日志记录指南
• 记录食谱

1. 工具和库：

• Loguru 文档
• Structlog 文档
• Python-JSON-Logger

本指南涵盖了 Python 日志记录的基本方面，从基本设置到高级实现。请记住，日志记录是应用程序可观察性和维护的一个组成部分。深思熟虑地实施并定期维护以获得最佳结果。

请记住，随着应用程序的发展和新需求的出现，定期检查和更新您的日志记录实现。

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