What is thread
Thread is the smallest unit that the operating system can perform operation scheduling
Included in the process, is the actual operation unit in the process
Multiple threads can be concurrent in a process, and each thread can perform different tasks
Multithreading is similar to executing multiple different programs at the same time
Advantage 1: Put long tasks in the background for processing
Advantage 2: The program may run faster
Python implements multithreading
Python provides thread and threading modules
threading is more advanced than thread module
Pass in a function and create a Thread instance, call the start method to execute
import threading#定义多线程执行函数def test(name,i):print(name+'执行:',i)#创建t1\t2两个线程t1 = threading.Thread(target = test,args=('线程一',100))t2 = threading.Thread(target = test,args=('线程二',200))#执行线程t1.start()t2.start()#线程同步t1.join()t2.join()
Thread pool usage
Threads and processes can manage multithreading through the thread pool
ThreadPoolExecutor implements thread pool
from concurrent.futures import ThreadPoolExecutor#定义多线程执行函数def test(name,i):print('线程'+name+'执行:',i)#创建多个线程thre_name = [] #定义线程池变量th_pool = ThreadPoolExecutor(max_workers = 2) #创建线程池，上限5个for i in range(10):thre_name.append(i)thre_name[i] = th_pool.submit(test,(thre_name[i],i))for i in range(0,len(thre_name)):print('线程%s是否完成'%i,thre_name[i].done())
The done() method is used to determine whether the thread has completed execution
Global Lock (GIL)
GIL generates mutex locks to restrict thread access to shared resources
GIL decides that multiple threads cannot call multiple CPU cores
Multi-threading is not recommended for CPU-intensive operations, multi-process is recommended
IO-intensive operations, multi-threading can significantly improve efficiency
Multithreading and'crawler' can be perfectly combined