在 Python 中，理解并发（Concurrency）、并行（Parallelism）、同步（Synchronization）、异步（Asynchronous）、阻塞（Blocking）和非阻塞（Non-blocking）是非常重要的，因为它们是构建高性能应用程序的关键概念。
1. 并发（Concurrency）
并发是指程序在同一时间段内可以处理多个任务的能力。具体来说，程序看起来像是同时执行多个任务，但实际上它们是在交替执行。
1.1 示例：多线程
import threading
import time
def worker():
print(f"Thread {threading.current_thread().name} started")
time.sleep(2)
print(f"Thread {threading.current_thread().name} finished")
# 创建多个线程
threads = []
for i in range(5):
thread = threading.Thread(target=worker, name=f"Thread-{i}")
threads.append(thread)
thread.start()
# 等待所有线程完成
for thread in threads:
thread.join()
print("All threads finished")2. 并行（Parallelism）
并行是指程序在同一时间可以真正同时执行多个任务的能力。通常需要硬件支持，例如多核处理器。
2.1 示例：多进程
import multiprocessing
def worker():
print(f"Process {multiprocessing.current_process().name} started")
time.sleep(2)
print(f"Process {multiprocessing.current_process().name} finished")
# 创建多个进程
processes = []
for i in range(5):
process = multiprocessing.Process(target=worker, name=f"Process-{i}")
processes.append(process)
process.start()
# 等待所有进程完成
for process in processes:
process.join()
print("All processes finished")3. 同步（Synchronization）
同步是指在多线程或多进程环境中，通过锁或其他机制确保资源的安全访问。
3.1 示例：锁（Lock）
import threading
def worker(lock):
with lock:
print(f"Thread {threading.current_thread().name} started")
time.sleep(2)
print(f"Thread {threading.current_thread().name} finished")
lock = threading.Lock()
# 创建多个线程
threads = []
for i in range(5):
thread = threading.Thread(target=worker, args=(lock,), name=f"Thread-{i}")
threads.append(thread)
thread.start()
# 等待所有线程完成
for thread in threads:
thread.join()
print("All threads finished")4. 异步（Asynchronous）
异步是指程序可以在等待某个操作完成的同时继续执行其他任务。异步编程通常使用回调函数或协程。
4.1 示例：异步 I/O（使用 asyncio）
import asyncio
async def worker():
print(f"Worker {asyncio.current_task().get_name()} started")
await asyncio.sleep(2)
print(f"Worker {asyncio.current_task().get_name()} finished")
async def main():
tasks = []
for i in range(5):
task = asyncio.create_task(worker(), name=f"Worker-{i}")
tasks.append(task)
await asyncio.gather(*tasks)
asyncio.run(main())5. 阻塞（Blocking）
阻塞是指程序在执行某个操作时会暂停执行，直到该操作完成。例如，当执行一个阻塞的 I/O 操作时，程序会等待直到 I/O 操作完成。
5.1 示例：阻塞 I/O
import time
def blocking_io():
print("Starting blocking IO")
time.sleep(5)
print("Finished blocking IO")
blocking_io()6. 非阻塞（Non-blocking）
非阻塞是指程序在执行某个操作时不会暂停执行，而是继续执行其他任务。通常用于网络 I/O 或文件 I/O。
6.1 示例：非阻塞 I/O（使用 select）
import select
import socket
server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
server_socket.bind(('localhost', 8000))
server_socket.listen(5)
sockets_list = [server_socket]
def handle_client(client_socket):
request = client_socket.recv(1024)
print(f"Received: {request.decode()}")
response = "Hello, World!\n"
client_socket.send(response.encode())
client_socket.close()
while True:
read_sockets, _, exception_sockets = select.select(sockets_list, [], sockets_list)
for notified_socket in read_sockets:
if notified_socket == server_socket:
client_socket, client_address = server_socket.accept()
sockets_list.append(client_socket)
else:
handle_client(notified_socket)
for notified_socket in exception_sockets:
sockets_list.remove(notified_socket)
notified_socket.close()1. 并发（Concurrency）
1.1 示例：多线程发送 HTTP 请求
import threading
import requests
import time
def send_request(url, headers, payload):
response = requests.post(url, headers=headers, jsnotallow=payload)
print(f"Response status code: {response.status_code}")
print(f"Response content: {response.text}")
# 测试数据
test_cases = [
{
"url": "https://api.example.com/v1/resource",
"headers": {"Authorization": "Bearer token1"},
"payload": {"key": "value1"}
},
{
"url": "https://api.example.com/v1/resource",
"headers": {"Authorization": "Bearer token2"},
"payload": {"key": "value2"}
}
]
# 创建线程列表
threads = []
# 创建并启动线程
for test_case in test_cases:
thread = threading.Thread(target=send_request, args=(test_case["url"], test_case["headers"], test_case["payload"]))
threads.append(thread)
thread.start()
# 等待所有线程完成
for thread in threads:
thread.join()
print("All requests finished")2. 并行（Parallelism）
2.1 示例：多进程发送 HTTP 请求
import multiprocessing
import requests
import time
def send_request(url, headers, payload):
response = requests.post(url, headers=headers, jsnotallow=payload)
print(f"Response status code: {response.status_code}")
print(f"Response content: {response.text}")
# 测试数据
test_cases = [
{
"url": "https://api.example.com/v1/resource",
"headers": {"Authorization": "Bearer token1"},
"payload": {"key": "value1"}
},
{
"url": "https://api.example.com/v1/resource",
"headers": {"Authorization": "Bearer token2"},
"payload": {"key": "value2"}
}
]
# 创建进程列表
processes = []
# 创建并启动进程
for test_case in test_cases:
process = multiprocessing.Process(target=send_request, args=(test_case["url"], test_case["headers"], test_case["payload"]))
processes.append(process)
process.start()
# 等待所有进程完成
for process in processes:
process.join()
print("All requests finished")3. 同步（Synchronization）
3.1 示例：使用锁同步多线程
import threading
import requests
import time
def send_request(lock, url, headers, payload):
with lock:
response = requests.post(url, headers=headers, jsnotallow=payload)
print(f"Response status code: {response.status_code}")
print(f"Response content: {response.text}")
# 测试数据
test_cases = [
{
"url": "https://api.example.com/v1/resource",
"headers": {"Authorization": "Bearer token1"},
"payload": {"key": "value1"}
},
{
"url": "https://api.example.com/v1/resource",
"headers": {"Authorization": "Bearer token2"},
"payload": {"key": "value2"}
}
]
# 创建锁
lock = threading.Lock()
# 创建线程列表
threads = []
# 创建并启动线程
for test_case in test_cases:
thread = threading.Thread(target=send_request, args=(lock, test_case["url"], test_case["headers"], test_case["payload"]))
threads.append(thread)
thread.start()
# 等待所有线程完成
for thread in threads:
thread.join()
print("All requests finished")4. 异步（Asynchronous）
4.1 示例：使用 asyncio 发送异步 HTTP 请求
import asyncio
import aiohttp
async def send_request(url, headers, payload):
async with aiohttp.ClientSession() as session:
async with session.post(url, headers=headers, jsnotallow=payload) as response:
print(f"Response status code: {response.status}")
response_text = await response.text()
print(f"Response content: {response_text}")
# 测试数据
test_cases = [
{
"url": "https://api.example.com/v1/resource",
"headers": {"Authorization": "Bearer token1"},
"payload": {"key": "value1"}
},
{
"url": "https://api.example.com/v1/resource",
"headers": {"Authorization": "Bearer token2"},
"payload": {"key": "value2"}
}
]
async def main():
tasks = []
for test_case in test_cases:
task = asyncio.create_task(send_request(test_case["url"], test_case["headers"], test_case["payload"]))
tasks.append(task)
await asyncio.gather(*tasks)
asyncio.run(main())5. 阻塞（Blocking）
5.1 示例：阻塞式发送 HTTP 请求
import requests
import time
def send_request(url, headers, payload):
response = requests.post(url, headers=headers, jsnotallow=payload)
print(f"Response status code: {response.status_code}")
print(f"Response content: {response.text}")
# 测试数据
test_cases = [
{
"url": "https://api.example.com/v1/resource",
"headers": {"Authorization": "Bearer token1"},
"payload": {"key": "value1"}
},
{
"url": "https://api.example.com/v1/resource",
"headers": {"Authorization": "Bearer token2"},
"payload": {"key": "value2"}
}
]
# 依次发送请求
for test_case in test_cases:
send_request(test_case["url"], test_case["headers"], test_case["payload"])
print("All requests finished")6. 非阻塞（Non-blocking）
6.1 示例：使用 select 发送非阻塞 HTTP 请求
import select
import socket
import requests
import time
def send_request(url, headers, payload):
response = requests.post(url, headers=headers, jsnotallow=payload)
print(f"Response status code: {response.status_code}")
print(f"Response content: {response.text}")
# 测试数据
test_cases = [
{
"url": "https://api.example.com/v1/resource",
"headers": {"Authorization": "Bearer token1"},
"payload": {"key": "value1"}
},
{
"url": "https://api.example.com/v1/resource",
"headers": {"Authorization": "Bearer token2"},
"payload": {"key": "value2"}
}
]
# 创建套接字列表
sockets_list = []
# 创建并启动套接字
for test_case in test_cases:
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.connect(("localhost", 8000))
sockets_list.append(sock)
# 监听套接字
while sockets_list:
ready_to_read, _, _ = select.select(sockets_list, [], [])
for sock in ready_to_read:
send_request(test_cases[sockets_list.index(sock)]["url"], test_cases[sockets_list.index(sock)]["headers"], test_cases[sockets_list.index(sock)]["payload"])
sockets_list.remove(sock)
print("All requests finished")7. 总结
通过以上示例，我们详细介绍了 Python 中的几个关键概念：
并发（Concurrency）：在同一时间段内处理多个任务。
并行（Parallelism）：在同一时间真正同时执行多个任务。
同步（Synchronization）：确保多线程或多进程环境下的资源安全访问。
异步（Asynchronous）：在等待某个操作完成的同时继续执行其他任务。
阻塞（Blocking）：在执行某个操作时会暂停执行，直到该操作完成。
非阻塞（Non-blocking）：在执行某个操作时不会暂停执行，而是继续执行其他任务。
