python multithreaded processing list

python how to traverse a list in multiple threads

You can traverse the elements of the second list to check if they appear in the second list, and if you use table comprehensions, you can use a single line of code to accomplish the task. list1=[1,2,3,4,5]list2=[4,5,6,7,8]print[ lforlinlist1iflinlist2]#[4,5]If there are no duplicate elements in each list,…

How to program Python multithreading, an article to read Python multithreading

This article is about Python multithreading, to understand Python multithreading, you have to understand what is a thread.

A thread is the smallest unit of computing scheduling that an operating system can perform. It is contained within a process and is the actual unit of operation within the process. A thread refers to a single sequential flow of control in a process, and multiple threads can be concurrent in a process, each performing a different task in parallel.

And multithreading is similar to executing several different programs at the same time. Multithreaded operation has the following advantages:

1. The use of threads allows you to put tasks in a program that occupy a long period of time into the background.

2. The user interface can be more attractive, so that, for example, if the user clicks on a button to trigger the processing of some event, a progress bar can pop up to show the progress of the processing

The program may run faster

3. Threads are more useful in the implementation of some waiting tasks, such as user input, file reading and writing, and sending and receiving data over the network. In this case we can free up some precious resources such as memory usage etc.

4. Threads are still different from processes in their execution. Each individual thread has an entry point for program execution, a sequence of sequential execution and an exit from the program. However, threads cannot execute independently and must be dependent on the application program, which provides multiple thread execution control.

Each thread has its own set of CPU registers, called the thread’s context, which reflects the state of the CPU registers where the thread last ran.

The instruction pointer and stack pointer registers are two of the most important registers in the thread’s context.Threads always run in the process get context.These addresses are used to flag memory in the address space of the process that owns the thread.

5. Threads can be preempted (interrupted).

6. Threads can be temporarily put on hold (also known as sleep) while other threads are running – this is called thread abdication.

Starting to learn Python multithreading

There are two ways to use threads in Python: functions or wrapping thread objects in classes.

Functional: call the start_new_thread() function in the thread module to spawn a new thread. The syntax is as follows:

thread.start_new_thread(function,args[,kwargs])Parameter description:

1. function-Thread function.

2. args-Arguments passed to the thread function, which must be of type tuple.

3.kwargs-Optional arguments.

Attached example

#! /usr/bin/python




#Define a function for thread

defprint_time(threadName, delay):





print”%s:%s”%(threadName,time.ctime(time .time()))

#Create two threads




thread. time,(“Thread-2”,4,))


print “Error:unabletostartthread”


passExecuting the above program outputs the following:

Thread-1. ThuJan2215:42:172009







Thread-2. ThuJan2215:42:272009


Thread-2:ThuJan2215:42:352009 The end of a thread generally relies on the natural termination of a thread function; it is also possible to call thread. exit() in the thread function, which throws a SystemExitexception to exit the thread.

How python implements multithreading

Threads are also known as lightweight processes, multithreading allows multiple threads to be executed at once, Python is a multithreaded language, it has a multithreading package, GIL which is also known as a Global Interpreter Lock to ensure that a single thread is executed at a time, a thread saves the GIL and performs some operations before passing it to the next thread, which also creates the the illusion of parallel execution.