207_Rust异步编程tokio¶

# Rust异步 核心概念示例

# 基础用法演示
print("=== Rust异步 基础示例 ===")

# 1. 初始化/配置
config = {
    "mode": "default",
    "timeout": 30,
    "retry": 3
}

# 2. 核心操作
def demonstrate_rust异步():
    """演示Rust异步的核心功能"""
    # 步骤1: 准备环境
    # 步骤2: 执行核心操作
    # 步骤3: 处理结果
    return "示例结果"

result = demonstrate_rust异步()
print(f"结果: {result}")

# 核心API和常用操作

# 创建/初始化
# 配置参数说明
# 错误处理模式

class Rust异步Handler:
    """处理Rust异步相关操作的封装类"""

    def __init__(self, config=None):
        self.config = config or {}
        self._initialized = False

    def initialize(self):
        """初始化资源"""
        self._initialized = True
        return self

    def process(self, data):
        """处理核心逻辑"""
        if not self._initialized:
            raise RuntimeError("未初始化")
        # 核心处理逻辑
        return {"status": "success", "data": data}

    def cleanup(self):
        """清理资源"""
        self._initialized = False

# 使用示例
handler = Rust异步Handler()
handler.initialize()
result = handler.process("test_data")
handler.cleanup()

# 高级用法和优化技巧

# 1. 性能优化
import time

def benchmark_rust异步(iterations=1000):
    """性能基准测试"""
    start = time.perf_counter()
    for _ in range(iterations):
        pass  # 待测操作
    elapsed = time.perf_counter() - start
    print(f"{iterations}次操作耗时: {elapsed:.4f}s")
    print(f"平均每次: {elapsed/iterations*1000:.4f}ms")

# 2. 错误处理与重试
def robust_operation(max_retries=3):
    """健壮的操作实现"""
    for attempt in range(max_retries):
        try:
            # 执行操作
            return "success"
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(2 ** attempt)  # 指数退避

# 3. 并发处理模式
from concurrent.futures import ThreadPoolExecutor

def parallel_process(items, max_workers=4):
    """并行处理"""
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        results = list(executor.map(lambda x: x, items))
    return results

# 实战案例

# 案例1: 基本业务场景
def business_scenario_1():
    """常见业务需求实现"""
    # 数据获取
    # 业务处理
    # 结果输出
    pass

# 案例2: 高可用场景
def high_availability_scenario():
    """生产环境高可用配置"""
    # 健康检查
    # 故障转移
    # 监控告警
    pass

# 案例3: 性能优化场景
def performance_scenario():
    """大规模数据处理"""
    # 批处理优化
    # 缓存策略
    # 资源管理
    pass

# 监控和调试工具

import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def monitored_operation():
    """带监控的操作"""
    logger.info("开始执行")
    try:
        # 核心操作
        result = "success"
        logger.info(f"执行成功: {result}")
        return result
    except Exception as e:
        logger.error(f"执行失败: {e}", exc_info=True)
        raise

# 性能分析
import cProfile

def profile_operation():
    """性能分析"""
    profiler = cProfile.Profile()
    profiler.enable()
    # 待分析的操作
    profiler.disable()
    profiler.print_stats(sort='cumulative')

# 安装与配置
# pip install <package>

# 常用运维命令
# 启动服务 / 检查状态 / 查看日志

# 调试命令
# python -m cProfile script.py
# python -m trace --count script.py

# 性能测试
# python -m timeit "expression"
# ab -n 1000 -c 10 http://localhost:8080/