feat(admin): 添加用户管理相关文件
添加用户管理视图、API和状态管理文件
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张翔
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#!/usr/bin/env python3
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"""
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本地并发控制模块演示脚本
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展示本地并发控制的核心功能。
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"""
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import time
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import threading
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from core.concurrency_control import (
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SemaphoreControl,
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ReadWriteLock,
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RateLimiter,
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LocalDistributedLock,
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ConcurrentCounter,
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ThreadBarrier,
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BoundedTaskQueue,
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concurrency_manager,
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)
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def demo_semaphore():
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"""演示信号量"""
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print("\n" + "="*60)
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print("演示1: 信号量并发控制")
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print("="*60)
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semaphore = SemaphoreControl(max_concurrent=3)
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active_count = [0]
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max_active = [0]
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lock = threading.Lock()
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def worker():
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with semaphore.acquire():
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with lock:
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active_count[0] += 1
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max_active[0] = max(max_active[0], active_count[0])
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time.sleep(0.2)
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with lock:
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active_count[0] -= 1
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threads = [threading.Thread(target=worker) for _ in range(10)]
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for t in threads:
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t.start()
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for t in threads:
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t.join()
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print(f"✅ 10个线程执行完成")
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print(f"✅ 最大并发数: {max_active[0]} (限制为3)")
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print(f"✅ 统计: {semaphore.get_stats()}")
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def demo_read_write_lock():
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"""演示读写锁"""
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print("\n" + "="*60)
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print("演示2: 读写锁")
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print("="*60)
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rw_lock = ReadWriteLock()
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data = {"value": 0}
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def reader():
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with rw_lock.read_lock():
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_ = data["value"]
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time.sleep(0.05)
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def writer():
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with rw_lock.write_lock():
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data["value"] += 1
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time.sleep(0.05)
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# 5个读线程
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read_threads = [threading.Thread(target=reader) for _ in range(5)]
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# 2个写线程
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write_threads = [threading.Thread(target=writer) for _ in range(2)]
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for t in read_threads + write_threads:
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t.start()
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for t in read_threads + write_threads:
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t.join()
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print(f"✅ 5个读线程 + 2个写线程执行完成")
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print(f"✅ 最终数据值: {data['value']}")
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def demo_rate_limiter():
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"""演示限流器"""
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print("\n" + "="*60)
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print("演示3: 限流器")
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print("="*60)
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limiter = RateLimiter(max_requests=5, time_window=1)
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# 前5个请求应该通过
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allowed = 0
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for i in range(5):
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if limiter.allow_request():
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allowed += 1
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print(f"✅ 前5个请求通过: {allowed}")
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# 超出限制应该被阻止
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blocked = 0
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for i in range(3):
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if not limiter.allow_request():
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blocked += 1
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print(f"✅ 超出限制被阻止: {blocked}")
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# 等待时间窗口重置
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print("⏱️ 等待1.1秒...")
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time.sleep(1.1)
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reset_allowed = 0
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for i in range(3):
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if limiter.allow_request():
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reset_allowed += 1
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print(f"✅ 重置后通过: {reset_allowed}")
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print(f"✅ 统计: {limiter.get_stats()}")
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def demo_distributed_lock():
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"""演示分布式锁"""
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print("\n" + "="*60)
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print("演示4: 分布式锁(本地模拟)")
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print("="*60)
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lock = LocalDistributedLock("test_resource")
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acquired_count = [0]
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lock_obj = threading.Lock()
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def try_acquire():
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if lock.acquire(timeout=0.5):
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with lock_obj:
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acquired_count[0] += 1
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time.sleep(0.2)
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lock.release()
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threads = [threading.Thread(target=try_acquire) for _ in range(5)]
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for t in threads:
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t.start()
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for t in threads:
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t.join()
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print(f"✅ 5个线程尝试获取锁")
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print(f"✅ 成功获取锁次数: {acquired_count[0]}")
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# 测试超时释放
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lock2 = LocalDistributedLock("test_resource2", expire_seconds=1)
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lock2.acquire()
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print("✅ 获取锁(1秒过期)")
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time.sleep(1.2)
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assert lock2.acquire(), "超时后应该可以重新获取锁"
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print("✅ 超时后成功重新获取锁")
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lock2.release()
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def demo_concurrent_counter():
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"""演示并发计数器"""
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print("\n" + "="*60)
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print("演示5: 并发计数器")
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print("="*60)
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counter = ConcurrentCounter(initial_value=0)
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def increment_worker():
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for _ in range(100):
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counter.increment()
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threads = [threading.Thread(target=increment_worker) for _ in range(10)]
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for t in threads:
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t.start()
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for t in threads:
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t.join()
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print(f"✅ 10个线程各递增100次")
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print(f"✅ 最终计数值: {counter.get_value()} (期望: 1000)")
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# 递减
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def decrement_worker():
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for _ in range(50):
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counter.decrement()
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threads = [threading.Thread(target=decrement_worker) for _ in range(5)]
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for t in threads:
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t.start()
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for t in threads:
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t.join()
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print(f"✅ 5个线程各递减50次")
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print(f"✅ 最终计数值: {counter.get_value()} (期望: 750)")
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def demo_barrier():
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"""演示屏障"""
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print("\n" + "="*60)
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print("演示6: 线程屏障")
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print("="*60)
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barrier = ThreadBarrier(parties=3)
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arrival_times = []
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lock = threading.Lock()
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def worker():
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time.sleep(0.05)
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barrier.wait()
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with lock:
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arrival_times.append(time.time())
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threads = [threading.Thread(target=worker) for _ in range(3)]
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start_time = time.time()
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for t in threads:
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t.start()
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for t in threads:
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t.join()
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max_diff = max(arrival_times) - min(arrival_times)
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print(f"✅ 3个线程通过屏障")
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print(f"✅ 最大到达时间差: {max_diff:.4f}s")
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def demo_bounded_queue():
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"""演示有界队列"""
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print("\n" + "="*60)
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print("演示7: 有界任务队列")
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print("="*60)
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queue = BoundedTaskQueue(max_size=3)
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# 添加3个任务
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for i in range(3):
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queue.put(f"task_{i}")
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print("✅ 添加3个任务")
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# 尝试添加第4个（应该阻塞）
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print("⏱️ 尝试添加第4个任务（应该超时）...")
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try:
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queue.put("overflow_task", timeout=0.1)
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print("⚠️ 添加成功（意外）")
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except TimeoutError:
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print("✅ 队列满时正确阻塞")
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# 消费任务
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tasks = []
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for _ in range(3):
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tasks.append(queue.get(timeout=0.1))
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print(f"✅ 消费任务: {tasks}")
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def demo_manager():
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"""演示并发管理器"""
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print("\n" + "="*60)
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print("演示8: 并发控制器管理器")
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print("="*60)
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# 创建命名信号量
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semaphore = concurrency_manager.create_semaphore("api_limit", max_concurrent=5)
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print("✅ 创建命名信号量: api_limit")
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# 获取已创建的组件
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retrieved = concurrency_manager.get_semaphore("api_limit")
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print(f"✅ 获取信号量: {retrieved is semaphore}")
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# 获取统计信息
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stats = concurrency_manager.get_all_stats()
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print(f"✅ 统计信息: {stats}")
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def main():
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"""主函数"""
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print("\n" + "="*60)
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print("本地并发控制模块演示")
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print("="*60)
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demo_semaphore()
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demo_read_write_lock()
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demo_rate_limiter()
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demo_distributed_lock()
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demo_concurrent_counter()
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demo_barrier()
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demo_bounded_queue()
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demo_manager()
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print("\n" + "="*60)
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print("✅ 所有演示完成!")
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print("="*60)
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if __name__ == "__main__":
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main()
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