You may have heard the terms "parallelization" or "concurrency", which refer to scheduling tasks to run parallelly or concurrently (at the same time) to save time and resources. This is a common practice in asynchronous programming, wherecoroutines are used to execute tasks concurrently.

Threading in Python is used to run multiple tasks at the same time, hence saving time and resources and increasing efficiency.

Although multi-threading can save time and resources by executing multiple tasks at the same time, using it in code can lead to safety and reliability issues.

In this article, you'll learn what is threading in Python and how you can use it to make multiple tasks run concurrently.

What is Threading?

Threading, as previously stated, refers to the concurrent execution of multiple tasks in a single process. This is accomplished by utilizing Python'sthreading module.

Threads are smaller units of the program that run concurrently and share the same memory space.

How to Create Threads and Execute Concurrently

Python provides a module calledthreading that provides a high-level threading interface to create and manage threads in Python programs.

Create and Start Thread

A thread can be created using theThread class provided by thethreading module. Using this class, you can create an instance of theThread and then start it using the.start() method.

importthreading# Creating Target Functiondefnum_gen(num):forninrange(num):print("Thread: ",n)# Main Code of the Programif__name__=="__main__":print("Statement: Creating and Starting a Thread.")thread=threading.Thread(target=num_gen,args=(3,))thread.start()print("Statement: Thread Execution Finished.")

A thread is created by instantiating theThread class with atarget parameter that takes a callable object in this case, thenum_gen function, and anargs parameter that accepts a list or tuple of arguments, in this case,3.

This means that you are tellingThread to run thenum_gen() function and pass3 as an argument.

If you run the code, you'll get the following output:

Statement: Creating and Starting a Thread.Statement: Thread Execution Finished.Thread:  0Thread:  1Thread:  2

You can notice that theStatement section of the code has finished before theThread did.Why does this happen?

The thread starts executing concurrently with the main program and the main program does not wait for the thread to finish before continuing its execution. That's why the above code resulted in executing theprint statement before the thread was finished.

To understand this, you need to understand the execution flow of the program:

• First, the"Statement: Creating and Starting a Thread." print statement is executed.

• Then the thread is created and started usingthread.start().

• The thread starts executing concurrently with the main program.

• The"Statement: Thread Execution Finished." print statement is executed by the main program.

• The thread continues and prints the output.

The thread and the main program run independently that's why their execution order is not fixed.

join() Method - The Saviour

Seeing the above situation, you might have thought then how to suspend the execution of the main program until the thread is finished executing.

Well, thejoin() method is used in that situation, it doesn't let e*xecute the code further until the current thread terminates*.

importthreading# Creating Target Functiondefnum_gen(num):forninrange(num):print("Thread: ",n)# Main Code of the Programif__name__=="__main__":print("Statement: Creating and Starting a Thread.")thread=threading.Thread(target=num_gen,args=(3,))thread.start()thread.join()print("Statement: Thread Execution Finished.")

After creating and starting a thread, thejoin() method is called on theThread instance (thread). Now run the code, and you'll get the following output.

Statement: Creating and Starting a Thread.Thread:  0Thread:  1Thread:  2Statement: Thread Execution Finished.

As can be seen, the"Statement: Thread Execution Finished." print statement is executed after the thread terminates.

Daemon Threads

Daemon threads run in the background and terminate immediately whether they completed the work or not when the main program exits.

You can make a daemon thread by passing thedaemon parameter when instantiating theThread class. You can pass a boolean value to indicate whether the thread is a daemon (True) or not (False).

importthreadingimporttimedefdaemon_thread():whileTrue:print("Daemon thread is running.")time.sleep(1)print("Daemon thread finished executing.")if__name__=="__main__":thread1=threading.Thread(target=daemon_thread,daemon=True)thread1.start()print("Main program exiting.")

A thread is created by instantiating theThread class passing thedaemon_thread function inside it and to mark it as adaemon thread, thedaemon parameter is set toTrue.

Thedaemon_thread() function is an infinite loop that prints a statement, sleeps for one second, and then again prints a statement.

Now when you run the above code, you'll get the following output.

Daemon thread is running.Main program exiting.

You can see that as soon as the main program exits, the daemon thread terminates.

At the time when thedaemon_thread() function enters the loop, the concurrently running main program exits, and thedaemon_thread() function never reaches the nextprint statement as can be seen in the output.

threading.Lock - Avoiding Race Conditions

Threads, as you know, run concurrently in a program. If your program has multiple threads, they may share the same resources or the critical section of the code at the same time, this type of condition is calledrace conditions.

This is where theLock comes into play, it acts like a synchronization barrier that prevents multiple threads from accessing the particular code or resources simultaneously.

The thread calls theacquire() method to acquire theLock and therelease() method to release theLock.

importthreading# Creating Lock instancelock=threading.Lock()data=""defread_file():globaldatawithopen("sample.txt","r")asfile:forinfoinfile:data+="\n"+infodeflock_task():lock.acquire()read_file()lock.release()if__name__=="__main__":thread1=threading.Thread(target=lock_task)thread2=threading.Thread(target=lock_task)thread1.start()thread2.start()thread1.join()thread2.join()# Printing the data read from the fileprint(f"Data:{data}")

First, aLock is created using thethreading.Lock() and store it inside thelock variable.

An empty string is created (data) for storing the information from both threads concurrently.

Theread_file() function is created that reads the information from thesample.txt file and adds it to the data.

Thelock_task() function is created and when it is called, the following events occur:

• Thelock.acquire() method will acquire the Lock immediately when thelock_task() function is called.

• If theLock is available, the program will execute theread_file() function.

• After theread_file() function finished executing, thelock.release() method will release theLock to make it available again for other threads.

Within theif __name__ == "__main__" block, two threads are createdthread1 andthread2 that both runs thelock_task() function.

Both threads run concurrently and attempt to access and execute theread_file() function at the same time but only one thread can access and enter theread_file() at a time due to theLock.

The main program waits for both threads to execute completely because ofthread1.join() andthread2.join().

Then using theprint statement, the information present in the file is printed.

Data: Hello there! Welcome to GeekPython.Hello there! Welcome to GeekPython.

As can be seen in the output, one thread at a time reads the file. However, there were two threads that's why the file was read two times, first bythread1 and then bythread2.

Semaphore Objects in Threading

Semaphore allows you to limit the number of threads that you want to access the shared resources simultaneously. Semaphore has two methods:

• acquire(): Thread can acquire the semaphore if it is available.When a thread acquires a semaphore,the semaphore's count decrement if it is greater than zero. If the count is zero, the thread waits until the semaphore is available.

• release(): After using the resources, thethread releases the semaphore that results in an increment in the count. This means that shared resources are available.

Semaphore is used to limit access to shared resources, preventing resource exhaustion and ensuring controlled access to resources with limited capacity.

importthreading# Creating a semaphoresem=threading.Semaphore(2)defthread_task(num):print(f"Thread{num}: Waiting")# Acquire the semaphoresem.acquire()print(f"Thread{num}: Acquired the semaphore")# Simulate some workfor_inrange(5):print(f"Thread{num}: In process")# Release the semaphore when donesem.release()print(f"Thread{num}: Released the semaphore.")if__name__=="__main__":thread1=threading.Thread(target=thread_task,args=(1,))thread2=threading.Thread(target=thread_task,args=(2,))thread3=threading.Thread(target=thread_task,args=(3,))thread1.start()thread2.start()thread3.start()thread1.join()thread2.join()thread3.join()print("All threads have finished.")

In the above code,Semaphore is instantiated with the integer value of2 which means two threads are allowed to run at the same time.

Three threads are created and all of them use thethread_task() function. But only two threads are allowed to run at the same time, so two threads will access and enter thethread_task() function at the same time, and when any of the threads releases the semaphore, the third thread will acquire the semaphore.

Thread 1: WaitingThread 1: Acquired the semaphoreThread 1: In processThread 1: In processThread 1: In processThread 1: In processThread 1: In processThread 2: WaitingThread 2: Acquired the semaphoreThread 1: Released the semaphore.Thread 2: In processThread 2: In processThread 3: WaitingThread 2: In processThread 3: Acquired the semaphoreThread 3: In processThread 2: In processThread 2: In processThread 2: Released the semaphore.Thread 3: In processThread 3: In processThread 3: In processThread 3: In processThread 3: Released the semaphore.All threads have finished.

Using ThreadPoolExecutor to Execute Tasks from a Pool of Worker Threads

TheThreadPoolExecutor is a part ofconcurrent.features module that is used to execute multiple tasks concurrently. UsingThreadPoolExecutor, you can run multiple tasks or functions concurrently without having to manually create and manage threads.

fromconcurrent.futuresimportThreadPoolExecutor# Creating pool of 4 threadsexecutor=ThreadPoolExecutor(max_workers=4)# Function to evaluate square numberdefsquare_num(num):print(f"Square of{num}:{num*num}.")task1=executor.submit(square_num,5)task2=executor.submit(square_num,2)task3=executor.submit(square_num,55)task5=executor.submit(square_num,4)# Wait for tasks to complete and then shutdownexecutor.shutdown()

The above code creates aThreadPoolExecutor with a maximum of4 worker threads which means the thread pool can have a maximum of 4 worker threads executing the tasks concurrently.

Four tasks are submitted to theThreadPoolExecutor using thesubmit method with thesquare_num() function and various arguments. This will execute the function with specified arguments and prints the output.

In the end, theshutdown method is called, so thatThreadPoolExecutor shutdowns after the tasks are completed and resources are freed.

You don't have to explicitly call theshutdown method if you createThreadPoolExecutor using thewith statement.

fromconcurrent.futuresimportThreadPoolExecutor# Taskdefsquare_num(num):print(f"Square of{num}:{num*num}.")# Using ThreadPoolExecutor as context managerwithThreadPoolExecutor(max_workers=4)asexecutor:task1=executor.submit(square_num,5)task2=executor.submit(square_num,2)task3=executor.submit(square_num,55)task5=executor.submit(square_num,4)

In the above code, theThreadPoolExecutor is used with thewith statement. When thewith block is exited, theThreadPoolExecutor is automatically shut down and its resources are released.

Both codes will produce the same result.

Square of 5: 25.Square of 2: 4.Square of 55: 3025.Square of 4: 16.

Common Function in Threading

Thethreading module provides numerous functions and some of them are explained below.

Getting Main and Current Thread

Thethreading module has amain_thread() and acurrent_thread() function which is used to get the main thread and the currently running thread respectively.

importthreadingdeftask():for_inrange(2):# Getting the current thread nameprint(f"Current Thread:{threading.current_thread().name} is running.")# Getting the main thread nameprint(f"Main thread   :{threading.main_thread().name} started.")thread1=threading.Thread(target=task)thread2=threading.Thread(target=task)thread1.start()thread2.start()thread1.join()thread2.join()print(f"Main thread   :{threading.main_thread().name} finished.")

Because themain_thread() andcurrent_thread() functions return aThread object,threading.main_thread().name is used toget the name of the main thread andthreading.current_thread().name is used toget the name of the current thread.

Main thread   : MainThread started.Current Thread: Thread-1(task) is running.Current Thread: Thread-1(task) is running.Current Thread: Thread-2(task) is running.Current Thread: Thread-2(task) is running.Main thread   : MainThread finished.

Monitoring Currently Active Threads

Thethreading.enumerate() function is used to return the list ofThread objects that are currently running. This includes the main thread even if it is terminated and excludes terminated threads and threads that have not started yet.

If you want to get the number ofThread objects that are currently alive, you can utilize thethreading.active_count() function.

importthreadingdeftask():print(f"Current Thread     :{threading.current_thread().name} is running.")# Getting the main thread nameprint(f"Main thread        :{threading.main_thread().name} started.")threads_list=[]for_inrange(5):thread=threading.Thread(target=task)thread.start()threads_list.append(thread)# Getting the active thread countprint(f"\nActive Thread Count:{threading.active_count()}")forthreadinthreads_list:thread.join()print(f"Main thread        :{threading.main_thread().name} finished.")# Getting the active thread countprint(f"Active Thread Count:{threading.active_count()}")# Getting the list of active threadsforactiveinthreading.enumerate():print(f"Active Thread List:{active.name}")

Output

Main thread        : MainThread started.Current Thread     : Thread-1(task) is running.Active Thread Count: 2Current Thread     : Thread-2(task) is running.Active Thread Count: 2Current Thread     : Thread-3(task) is running.Active Thread Count: 2Current Thread     : Thread-4(task) is running.Active Thread Count: 2Current Thread     : Thread-5(task) is running.Active Thread Count: 1Main thread        : MainThread finished.Active Thread Count: 1Active Thread List: MainThread

Getting Thread Id

importthreadingimporttimedeftask():print(f"Thread{threading.get_ident()} is running.")time.sleep(1)print(f"Thread{threading.get_ident()} is terminated.")print(f"Main thread started.")threads_list=[]for_inrange(5):thread=threading.Thread(target=task)thread.start()threads_list.append(thread)forthreadinthreads_list:thread.join()print(f"Main thread finished.")

Every thread running in a process is assigned an identifier and thethreading.get_ident() function is used to retrieve the identifier of the currently running thread.

Main thread started.Thread 9824 is running.Thread 7188 is running.Thread 4616 is running.Thread 3264 is running.Thread 7716 is running.Thread 7716 is terminated.Thread 9824 is terminated.Thread 7188 is terminated.Thread 4616 is terminated.Thread 3264 is terminated.Main thread finished.

Conclusion

A thread is a smaller unit in the program that is created using thethreading module in Python. Threads are tasks or functions that you can use multiple times in your program to execute concurrently to save time and resources.

In this article, you've learned:

• What is threading and how do you create and start a thread

• Whyjoin() method is used

• What aredaemon threads and how to create one

• How to Lock threads to avoid race conditions

• Howsemaphore is used to limit the number of threads that can access the shared resources at the same time.

• How you can execute a group of tasks using theThreadPoolExecutor without having to create threads.

• Some common functions provided by thethreading module.

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That's all for now

Keep Coding✌✌