告别Print: Python调试入门，用PDB高效找Bug

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告别Print: Python调试入门，用PDB高效找Bug

1. 为什么需要专业的调试工具

1.1 Print调试的局限性

在Python开发过程中，很多开发者习惯使用print()语句进行调试，这种方法虽然简单直接，但存在诸多局限性：

# 典型的print调试示例
def calculate_stats(data):print(f"输入数据: {data}")  # 调试语句1mean = sum(data) / len(data)print(f"计算均值: {mean}")  # 调试语句2variance = sum((x - mean) ** 2 for x in data) / len(data)print(f"计算方差: {variance}")  # 调试语句3std_dev = variance ** 0.5print(f"最终结果: {std_dev}")  # 调试语句4return std_dev# 测试代码
data = [1, 2, 3, 4, 5]
result = calculate_stats(data)
print(f"最终输出: {result}")

Print调试的主要问题：

1. 代码污染：调试语句与业务逻辑混杂
2. 效率低下：需要频繁添加、删除print语句
3. 信息有限：只能看到输出值，无法观察程序状态
4. 难以调试复杂问题：对于循环、递归、异常处理等场景力不从心

1.2 专业调试工具的优势

专业的调试工具如PDB（Python Debugger）提供了以下优势：

• 断点设置：在特定位置暂停程序执行
• 变量检查：实时查看和修改变量值
• 步进执行：逐行或逐函数执行代码
• 调用栈查看：了解函数调用关系
• 条件断点：满足特定条件时暂停
• 事后调试：分析程序崩溃后的状态

2. PDB基础入门

2.1 PDB简介

PDB是Python的标准调试器，无需安装额外包，是Python标准库的一部分。它提供了交互式的调试环境，可以帮助开发者深入理解代码执行过程。

2.2 启动PDB的三种方式

2.2.1 命令行直接启动

# 直接调试Python脚本
python -m pdb script.py# 示例：调试一个简单的计算器程序
python -m pdb calculator.py

2.2.2 在代码中嵌入断点

# 方法1：使用pdb.set_trace()
import pdbdef faulty_function(x, y):result = x + ypdb.set_trace()  # 在此处进入调试器result *= 2return result# 方法2：Python 3.7+ 使用breakpoint()
def modern_function(x, y):result = x + ybreakpoint()  # 等同于pdb.set_trace()，但更现代result *= 2return result

2.2.3 事后调试

import pdb
import tracebackdef problematic_function():try:# 可能出错的代码x = 1 / 0except Exception as e:print("发生异常，进入调试模式:")traceback.print_exc()pdb.post_mortem()  # 事后调试，检查异常发生时的状态problematic_function()

3. PDB核心命令详解

3.1 基本调试命令

# 演示用的示例代码：complex_calculation.py
def factorial(n):"""计算阶乘"""if n <= 1:return 1else:return n * factorial(n - 1)def calculate_combinations(n, k):"""计算组合数C(n, k)"""# 设置断点import pdb; pdb.set_trace()if k < 0 or k > n:return 0numerator = factorial(n)denominator = factorial(k) * factorial(n - k)result = numerator // denominatorreturn result# 测试
if __name__ == "__main__":print("计算C(5, 2):")result = calculate_combinations(5, 2)print(f"结果: {result}")

常用PDB命令表：

命令	缩写	功能描述
help	h	查看帮助信息
next	n	执行下一行
step	s	进入函数调用
continue	c	继续执行直到下一个断点
print	p	打印变量值
list	l	显示当前代码位置
return	r	执行直到当前函数返回
where	w	显示调用栈
quit	q	退出调试器

3.2 高级调试技巧

3.2.1 条件断点

def process_data(data_list):"""处理数据列表，在特定条件下设置断点"""results = []for i, item in enumerate(data_list):# 条件断点：当i等于3时暂停if i == 3:import pdb; pdb.set_trace()# 处理数据processed = item * 2 + 10# 另一个条件：当处理结果大于50时暂停if processed > 50:import pdb; pdb.set_trace()results.append(processed)return results# 更优雅的条件断点设置
def smart_breakpoint(condition, *args):"""智能断点函数"""if condition:breakpoint()def improved_processor(data_list):results = []for i, item in enumerate(data_list):# 使用智能断点smart_breakpoint(i == 2, f"索引i={i}")smart_breakpoint(item > 100, f"数据项={item}")results.append(item ** 2)return results

3.2.2 观察点（Watchpoint）

class DataProcessor:def __init__(self):self.counter = 0self.data = []def add_data(self, value):"""添加数据并计数"""self.data.append(value)self.counter += 1# 当counter达到特定值时调试if self.counter == 5:breakpoint()  # 观察计数器变化def process(self):"""处理数据"""total = sum(self.data)# 观察总和变化if total > 100:breakpoint()return total# 使用示例
processor = DataProcessor()
for i in range(10):processor.add_data(i * 10)
result = processor.process()

4. 实战：调试复杂程序

4.1 调试递归函数

# recursive_debug.py
def fibonacci(n, depth=0):"""计算斐波那契数列（带调试信息）"""indent = "  " * depthprint(f"{indent}fibonacci({n})被调用")# 设置条件断点：在特定深度或数值时暂停if depth == 3 or n == 2:breakpoint()if n <= 1:print(f"{indent}返回: {n}")return nelse:result = fibonacci(n-1, depth+1) + fibonacci(n-2, depth+1)print(f"{indent}返回: {result}")return resultdef debug_fibonacci():"""专门用于调试的包装函数"""print("开始计算斐波那契数列:")result = fibonacci(5)print(f"最终结果: {result}")return resultif __name__ == "__main__":debug_fibonacci()

4.2 调试面向对象程序

# oop_debug.py
class BankAccount:def __init__(self, account_holder, initial_balance=0):self.account_holder = account_holderself._balance = initial_balanceself.transaction_history = []print(f"账户创建: {account_holder}, 初始余额: {initial_balance}")def deposit(self, amount):"""存款"""if amount <= 0:raise ValueError("存款金额必须大于0")self._balance += amountself.transaction_history.append(('deposit', amount))# 调试点：大额存款时暂停if amount > 1000:breakpoint()print(f"存款: {amount}, 新余额: {self._balance}")return self._balancedef withdraw(self, amount):"""取款"""if amount <= 0:raise ValueError("取款金额必须大于0")if amount > self._balance:raise ValueError("余额不足")self._balance -= amountself.transaction_history.append(('withdraw', amount))# 调试点：余额低于阈值时暂停if self._balance < 100:breakpoint()print(f"取款: {amount}, 新余额: {self._balance}")return self._balancedef transfer(self, amount, target_account):"""转账"""print(f"开始转账: {amount} 到 {target_account.account_holder}")# 调试点：转账操作breakpoint()self.withdraw(amount)target_account.deposit(amount)self.transaction_history.append(('transfer', amount, target_account.account_holder))print("转账完成")@propertydef balance(self):return self._balancedef test_bank_account():"""测试银行账户功能"""# 创建账户alice = BankAccount("Alice", 1000)bob = BankAccount("Bob", 500)# 执行操作alice.deposit(200)alice.withdraw(100)alice.transfer(300, bob)# 检查最终状态print(f"Alice余额: {alice.balance}")print(f"Bob余额: {bob.balance}")print(f"Alice交易记录: {alice.transaction_history}")if __name__ == "__main__":test_bank_account()

5. 高级PDB技巧

5.1 PDB配置与自定义

# .pdbrc.py - PDB启动配置文件
import pdbdef setup_pdb():"""PDB配置函数"""# 自定义显示格式pdb.DefaultConfig.stdin = sys.stdinpdb.DefaultConfig.stdout = sys.stdoutpdb.DefaultConfig.use_terminal = True# 自定义提示符pdb.DefaultConfig.prompt = "(我的调试器) "# 自动导入常用模块import osimport sysprint("PDB调试环境已初始化")# 常用的调试辅助函数
def debug_vars(local_vars):"""调试变量显示函数"""print("=== 变量状态 ===")for name, value in local_vars.items():if not name.startswith('_'):print(f"{name}: {value}")def whereami():"""显示当前位置"""import inspectframe = inspect.currentframe().f_backprint(f"文件: {frame.f_code.co_filename}")print(f"行号: {frame.f_lineno}")print(f"函数: {frame.f_code.co_name}")# 在PDB中使用的便捷命令
class DebugCommands:@staticmethoddef show_locals():"""显示所有局部变量"""import inspectframe = inspect.currentframe().f_back.f_backlocals_dict = frame.f_localsdebug_vars(locals_dict)@staticmethoddef show_globals():"""显示所有全局变量"""import inspectframe = inspect.currentframe().f_back.f_backglobals_dict = frame.f_globalsdebug_vars(globals_dict)

5.2 远程调试

# remote_debug.py
import pdb
import socket
import threadingclass RemoteDebugger:"""远程调试器类"""def __init__(self, host='localhost', port=4444):self.host = hostself.port = portself.debugger = Nonedef start_remote_debug(self):"""启动远程调试服务"""def debug_server():# 创建socket服务器server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)server.bind((self.host, self.port))server.listen(1)print(f"远程调试服务启动在 {self.host}:{self.port}")print("等待调试客户端连接...")client, addr = server.accept()print(f"调试客户端已连接: {addr}")# 重定向输入输出到socketsys.stdin = client.makefile('r')sys.stdout = client.makefile('w')sys.stderr = client.makefile('w')# 启动PDBself.debugger = pdb.Pdb(stdin=sys.stdin, stdout=sys.stdout)self.debugger.set_trace()# 在新线程中运行调试服务器debug_thread = threading.Thread(target=debug_server)debug_thread.daemon = Truedebug_thread.start()def set_breakpoint(self, frame=None):"""设置远程断点"""if self.debugger:self.debugger.set_trace(frame)# 使用示例
def complex_algorithm(data):"""复杂的算法函数"""results = []# 设置远程调试断点debugger = RemoteDebugger()debugger.start_remote_debug()for i, item in enumerate(data):# 业务逻辑processed = item ** 2 + item * 2 + 1# 在特定条件下触发远程调试if processed > 1000:debugger.set_breakpoint()results.append(processed)return results

6. PDB与其他工具集成

6.1 与IDE调试器对比

# ide_integration.py
"""
演示如何在不同IDE中使用调试功能
"""def compare_debugging_methods():"""比较不同调试方法"""methods = {"PDB": {"优点": ["无需IDE", "服务器环境可用", "灵活性强"],"缺点": ["命令行操作", "学习曲线较陡"],"适用场景": ["生产环境调试", "远程调试", "简单脚本"]},"PyCharm调试器": {"优点": ["图形化界面", "功能丰富", "易用性好"],"缺点": ["需要IDE", "资源占用较多"],"适用场景": ["开发环境", "复杂项目", "团队协作"]},"VSCode调试器": {"优点": ["轻量级", "配置灵活", "扩展性强"],"缺点": ["需要配置", "功能相对简单"],"适用场景": ["跨平台开发", "轻量级项目"]}}return methodsdef integrated_debugging_example():"""集成调试示例"""data = [1, 2, 3, 4, 5]results = []try:for i, item in enumerate(data):# 复杂的处理逻辑result = item ** 2# 条件调试：根据不同环境选择调试方法if os.getenv('DEBUG_METHOD') == 'PDB':import pdb; pdb.set_trace()elif os.getenv('DEBUG_METHOD') == 'IDE':# 这里可以设置IDE的断点passresults.append(result)except Exception as e:# 异常处理中的调试print(f"发生异常: {e}")if os.getenv('POST_MORTEM_DEBUG', 'false').lower() == 'true':import pdb; pdb.post_mortem()return results

6.2 与测试框架结合

# test_debug_integration.py
import unittest
import pdb
import sysclass TestDebugIntegration(unittest.TestCase):"""测试与调试的集成"""def setUp(self):"""测试准备"""self.test_data = [1, 2, 3, 4, 5]def debug_test_failure(self, func, *args):"""调试测试失败的函数"""try:result = func(*args)return resultexcept Exception as e:print(f"测试失败，进入调试模式: {e}")tb = sys.exc_info()[2]pdb.post_mortem(tb)raisedef test_complex_calculation(self):"""测试复杂计算"""def complex_calc(data):results = []for item in data:# 可能出错的复杂计算if item == 0:raise ValueError("遇到零值")result = 100 / item + item ** 2results.append(result)return results# 使用调试包装器result = self.debug_test_failure(complex_calc, self.test_data)self.assertEqual(len(result), 5)def test_with_breakpoint(self):"""带断点的测试"""def problematic_function(x):# 设置条件断点if x > 10:breakpoint()return x * 2# 这个测试会在特定条件下触发调试器result = problematic_function(15)self.assertEqual(result, 30)def run_tests_with_debug():"""带调试的测试运行器"""# 创建测试套件loader = unittest.TestLoader()suite = loader.loadTestsFromTestCase(TestDebugIntegration)# 运行测试runner = unittest.TextTestRunner(verbosity=2)try:result = runner.run(suite)return result.wasSuccessful()except Exception as e:print(f"测试运行异常: {e}")return Falseif __name__ == "__main__":run_tests_with_debug()

7. 完整调试示例项目

# complete_debug_example.py
"""
完整的调试示例：一个数据处理管道
"""import pdb
import logging
from typing import List, Dict, Any
import json# 配置日志
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)class DataProcessingPipeline:"""数据处理管道类"""def __init__(self):self.stages = []self.context = {}self.debug_enabled = Falsedef add_stage(self, stage_func, name=None):"""添加处理阶段"""stage_info = {'func': stage_func,'name': name or stage_func.__name__}self.stages.append(stage_info)logger.debug(f"添加处理阶段: {stage_info['name']}")def enable_debug(self, enabled=True):"""启用调试模式"""self.debug_enabled = enabledif enabled:logger.info("调试模式已启用")def execute(self, input_data: List[Dict]) -> List[Dict]:"""执行数据处理管道"""logger.info(f"开始处理数据，共{len(input_data)}条记录")current_data = input_data.copy()for i, stage in enumerate(self.stages):stage_name = stage['name']stage_func = stage['func']logger.info(f"执行阶段 {i+1}/{len(self.stages)}: {stage_name}")try:# 调试点：每个阶段开始前if self.debug_enabled:logger.debug(f"阶段开始前调试点")self._debug_checkpoint(f"before_stage_{i}", current_data)# 执行处理阶段current_data = stage_func(current_data, self.context)# 调试点：每个阶段结束后if self.debug_enabled:logger.debug(f"阶段结束后调试点")self._debug_checkpoint(f"after_stage_{i}", current_data)logger.info(f"阶段完成: {stage_name}")except Exception as e:logger.error(f"阶段执行失败: {stage_name}, 错误: {e}")logger.info("进入调试模式...")pdb.post_mortem()raiselogger.info("数据处理管道执行完成")return current_datadef _debug_checkpoint(self, checkpoint_name, data):"""调试检查点"""print(f"\n=== 调试检查点: {checkpoint_name} ===")print(f"数据条数: {len(data)}")if data:sample = data[0] if isinstance(data, list) else dataprint(f"数据样本: {json.dumps(sample, indent=2, ensure_ascii=False)}")# 交互式调试if self.debug_enabled:response = input("继续执行? (y/n/debug): ").lower()if response == 'n':raise KeyboardInterrupt("用户中断执行")elif response == 'debug':breakpoint()# 具体的处理阶段函数
def stage_clean_data(data, context):"""数据清洗阶段"""logger.debug("开始数据清洗")cleaned_data = []for record in data:# 简单的数据清洗cleaned_record = {k: v for k, v in record.items() if v is not None}# 调试点：检查特定条件if len(cleaned_record) == 0:logger.warning("发现空记录")if context.get('debug_empty_records', False):breakpoint()cleaned_data.append(cleaned_record)return cleaned_datadef stage_calculate_stats(data, context):"""统计计算阶段"""logger.debug("开始统计计算")if not data:return data# 计算基本统计信息numeric_fields = []for record in data:for key, value in record.items():if isinstance(value, (int, float)) and key not in numeric_fields:numeric_fields.append(key)# 为每个数值字段计算统计量stats = {}for field in numeric_fields:values = [record.get(field, 0) for record in data if field in record]if values:stats[field] = {'count': len(values),'mean': sum(values) / len(values),'min': min(values),'max': max(values)}context['statistics'] = statslogger.debug(f"计算统计信息: {stats}")return datadef stage_enrich_data(data, context):"""数据增强阶段"""logger.debug("开始数据增强")enriched_data = []for i, record in enumerate(data):# 添加索引信息record['_index'] = irecord['_processed'] = True# 调试点：处理特定记录时暂停if i == len(data) // 2:  # 处理到中间记录时logger.debug(f"处理中间记录: {i}")if context.get('debug_midpoint', False):breakpoint()enriched_data.append(record)return enriched_datadef main():"""主函数：演示完整的数据处理管道"""# 创建测试数据sample_data = [{'name': 'Alice', 'age': 25, 'score': 85.5},{'name': 'Bob', 'age': 30, 'score': 92.0},{'name': 'Charlie', 'age': None, 'score': 78.5},  # 包含空值{'name': 'Diana', 'age': 28, 'score': None},      # 包含空值{'name': 'Eve', 'age': 35, 'score': 88.0}]# 创建处理管道pipeline = DataProcessingPipeline()pipeline.enable_debug(True)  # 启用调试模式# 配置调试上下文pipeline.context = {'debug_empty_records': True,'debug_midpoint': True}# 添加处理阶段pipeline.add_stage(stage_clean_data, "数据清洗")pipeline.add_stage(stage_calculate_stats, "统计计算")pipeline.add_stage(stage_enrich_data, "数据增强")try:# 执行管道result = pipeline.execute(sample_data)# 输出结果print("\n=== 处理结果 ===")for record in result:print(json.dumps(record, indent=2, ensure_ascii=False))print(f"\n=== 统计信息 ===")print(json.dumps(pipeline.context['statistics'], indent=2, ensure_ascii=False))except Exception as e:logger.error(f"管道执行失败: {e}")print("进入事后调试...")pdb.post_mortem()if __name__ == "__main__":main()

8. 调试最佳实践与工作流

8.1 系统化的调试方法

# debugging_workflow.py
"""
系统化的调试工作流实现
"""class SystematicDebugger:"""系统化调试器"""def __init__(self):self.breakpoints = {}self.watchpoints = {}self.history = []def add_breakpoint(self, condition_func, description=""):"""添加断点"""bp_id = len(self.breakpoints) + 1self.breakpoints[bp_id] = {'condition': condition_func,'description': description,'hit_count': 0}return bp_iddef add_watchpoint(self, variable_name, condition_func, description=""):"""添加观察点"""wp_id = len(self.watchpoints) + 1self.watchpoints[wp_id] = {'variable': variable_name,'condition': condition_func,'description': description,'hit_count': 0}return wp_iddef check_breakpoints(self, local_vars, global_vars):"""检查断点条件"""for bp_id, bp_info in self.breakpoints.items():try:if bp_info['condition'](local_vars, global_vars):bp_info['hit_count'] += 1self.history.append({'type': 'breakpoint','id': bp_id,'description': bp_info['description'],'timestamp': self._get_timestamp(),'variables': local_vars.copy()})print(f"\n*** 断点命中: {bp_info['description']} ***")print(f"命中次数: {bp_info['hit_count']}")breakpoint()except Exception as e:print(f"断点检查错误: {e}")def debug_function(self, func):"""调试装饰器"""def wrapper(*args, **kwargs):# 记录函数调用call_info = {'function': func.__name__,'args': args,'kwargs': kwargs,'timestamp': self._get_timestamp()}self.history.append(call_info)# 执行前检查断点frame = sys._getframe()self.check_breakpoints(frame.f_locals, frame.f_globals)try:result = func(*args, **kwargs)return resultexcept Exception as e:# 异常处理error_info = {'function': func.__name__,'exception': str(e),'timestamp': self._get_timestamp()}self.history.append(error_info)print(f"函数 {func.__name__} 执行异常: {e}")pdb.post_mortem()raisereturn wrapperdef _get_timestamp(self):"""获取时间戳"""from datetime import datetimereturn datetime.now().isoformat()def print_debug_report(self):"""打印调试报告"""print("\n" + "="*50)print("调试报告")print("="*50)print(f"\n断点统计:")for bp_id, bp_info in self.breakpoints.items():print(f"  {bp_id}: {bp_info['description']} - 命中 {bp_info['hit_count']} 次")print(f"\n调用历史 (最近10次):")for i, event in enumerate(self.history[-10:]):print(f"  {i+1}. {event}")# 使用示例
def demo_systematic_debugging():"""演示系统化调试"""debugger = SystematicDebugger()# 定义断点条件def bp_high_value(locals, globals):return 'result' in locals and locals['result'] > 100def bp_negative_input(locals, globals):return 'x' in locals and locals['x'] < 0# 添加断点debugger.add_breakpoint(bp_high_value, "结果值大于100")debugger.add_breakpoint(bp_negative_input, "输入值为负数")# 调试函数@debugger.debug_functiondef complex_calculation(x, y):result = x * y + (x + y) ** 2debugger.check_breakpoints(locals(), globals())return result# 测试调用test_cases = [(2, 3), (10, 5), (-1, 10), (8, 12)]for x, y in test_cases:print(f"\n计算: complex_calculation({x}, {y})")try:result = complex_calculation(x, y)print(f"结果: {result}")except Exception as e:print(f"错误: {e}")# 生成调试报告debugger.print_debug_report()if __name__ == "__main__":demo_systematic_debugging()

9. 调试性能优化

9.1 生产环境调试策略

# production_debug.py
"""
生产环境调试策略
"""import logging
import time
from functools import wrapsdef production_debug(enable_tracing=False, slow_threshold=1.0):"""生产环境调试装饰器"""def decorator(func):@wraps(func)def wrapper(*args, **kwargs):start_time = time.time()logger = logging.getLogger(func.__module__)# 记录函数开始if enable_tracing:logger.debug(f"函数开始: {func.__name__}")try:result = func(*args, **kwargs)execution_time = time.time() - start_time# 检查执行时间if execution_time > slow_threshold:logger.warning(f"函数执行缓慢: {func.__name__}, "f"耗时: {execution_time:.2f}秒")if enable_tracing:logger.debug(f"函数完成: {func.__name__}")return resultexcept Exception as e:execution_time = time.time() - start_timelogger.error(f"函数执行错误: {func.__name__}, "f"错误: {e}, 耗时: {execution_time:.2f}秒")# 生产环境下的安全调试if os.getenv('ENABLE_PRODUCTION_DEBUG', 'false').lower() == 'true':logger.info("进入生产环境调试模式")import pdbpdb.post_mortem()raisereturn wrapperreturn decoratorclass PerformanceMonitor:"""性能监控器"""def __init__(self):self.metrics = {}self.logger = logging.getLogger('performance')def track_performance(self, operation_name):"""性能跟踪装饰器"""def decorator(func):@wraps(func)def wrapper(*args, **kwargs):start_time = time.time()start_memory = self._get_memory_usage()try:result = func(*args, **kwargs)return resultfinally:end_time = time.time()end_memory = self._get_memory_usage()execution_time = end_time - start_timememory_used = end_memory - start_memory# 记录指标self._record_metrics(operation_name, execution_time, memory_used)# 检查性能问题self._check_performance_issues(operation_name, execution_time, memory_used)return wrapperreturn decoratordef _get_memory_usage(self):"""获取内存使用量"""import psutilprocess = psutil.Process()return process.memory_info().rss / 1024 / 1024  # MBdef _record_metrics(self, operation, time_used, memory_used):"""记录性能指标"""if operation not in self.metrics:self.metrics[operation] = {'count': 0,'total_time': 0,'max_time': 0,'total_memory': 0,'max_memory': 0}metrics = self.metrics[operation]metrics['count'] += 1metrics['total_time'] += time_usedmetrics['max_time'] = max(metrics['max_time'], time_used)metrics['total_memory'] += memory_usedmetrics['max_memory'] = max(metrics['max_memory'], memory_used)def _check_performance_issues(self, operation, time_used, memory_used):"""检查性能问题"""# 时间阈值检查if time_used > 5.0:  # 5秒阈值self.logger.warning(f"操作 {operation} 执行时间过长: {time_used:.2f}秒")# 内存阈值检查if memory_used > 100:  # 100MB阈值self.logger.warning(f"操作 {operation} 内存使用过多: {memory_used:.2f}MB")def generate_report(self):"""生成性能报告"""report = ["性能监控报告:", "="*50]for operation, metrics in self.metrics.items():avg_time = metrics['total_time'] / metrics['count']avg_memory = metrics['total_memory'] / metrics['count']report.extend([f"操作: {operation}",f"  调用次数: {metrics['count']}",f"  平均时间: {avg_time:.3f}秒",f"  最大时间: {metrics['max_time']:.3f}秒",f"  平均内存: {avg_memory:.2f}MB",f"  最大内存: {metrics['max_memory']:.2f}MB",""])return "\n".join(report)# 使用示例
def demo_production_debugging():"""演示生产环境调试"""monitor = PerformanceMonitor()@monitor.track_performance("数据计算")@production_debug(enable_tracing=True, slow_threshold=0.5)def heavy_calculation(n):"""耗时的计算函数"""result = 0for i in range(n):result += i ** 2time.sleep(0.01)  # 模拟耗时操作return result# 测试调用for i in [100, 200, 300]:print(f"计算 n={i}")result = heavy_calculation(i)print(f"结果: {result}")# 生成报告print(monitor.generate_report())if __name__ == "__main__":demo_production_debugging()

10. 总结

通过本文的全面介绍，我们深入探讨了Python PDB调试器的强大功能和使用技巧。从基础的断点设置到高级的远程调试，从简单的脚本调试到复杂的生产环境调试，PDB为Python开发者提供了全面的调试