Currently, the most widely used programming paradigm for the management of concurrence in software applications is based on multithreading. Generally, an application is made by a single process that is divided into multiple independent threads, which represent activities of different types that run parallel and compete with each other.

Although such a style of programming can lead to disadvantages of use and problems that need to be solved, modern applications with the mechanism of multithreading are still used quite widely.

Practically, all the existing operating systems support multithreading, and in almost all programming languages, there are mechanisms that you can use to implement concurrent applications through the use of threads.

Therefore, multithreaded programming is definitely a good choice to achieve concurrent applications. However, it is not the only choice available—there are several other alternatives, some of which, inter alia, perform better on the definition of...

Python manages a thread via the threading package that is provided by the Python standard library. This module provides some very interesting features that make the threading-based approach a whole lot easier; in fact, the threading module provides several synchronization mechanisms that are very simple to implement.

The major components of the threading module are:

In the following recipes, we examine the features offered by the threading library with different application examples. For the examples that follow, we will refer to the Python distribution 3.3 (even though Python 2.7 could be used).

The simplest way to use a thread is to instantiate it with a target function and then call the start() method to let it begin its work. The Python module threading has the Thread() method that is used to run processes and functions in a different thread:

class threading.Thread(group=None,
                       target=None,
                       name=None,
                       args=(),
                       kwargs={}) 

In the preceding code:

It is useful to spawn a thread and pass arguments to it that tell it what work...

Using arguments to identify or name the thread is cumbersome and unnecessary. Each Thread instance has a name with a default value that can be changed as the thread is created. Naming threads is useful in server processes with multiple service threads that handle different operations.

import threading
import time

def first_function():
    print (threading.currentThread().getName()+\
           str(' is Starting \n'))
    time.sleep(2)
    print (threading.currentThread().getName()+\
           str( ' is Exiting \n'))
    return

def second_function():
    print (threading.currentThread().getName()+\
           str(' is Starting \n'))
    time.sleep(2)
    print (threading.currentThread().getName()+\
           str( ' is Exiting \n'))
    return

def third_function():
    print (threading.currentThread...

To implement a new thread using the threading module, you have to do the following:

Once you have created the new Thread subclass, you can create an instance of it and then start a new thread by invoking the start() method, which will, in turn, call the run() method.

import threading
import time

exitFlag = 0

class myThread (threading.Thread):
    def __init__(self, threadID, name, counter):
        threading.Thread.__init__(self)
        self.threadID = threadID
        self.name = name
        self.counter = counter
    def run(self):
        print ("Starting " + self.name)
        print_time...

When two or more operations belonging to concurrent threads try to access the shared memory and at least one of them has the power to change the status of the data without a proper synchronization mechanism a race condition can occur and it can produce invalid code execution and bugs and unexpected behavior. The easiest way to get around the race conditions is the use of a lock. The operation of a lock is simple; when a thread wants to access a portion of shared memory, it must necessarily acquire a lock on that portion prior to using it. In addition to this, after completing its operation, the thread must release the lock that was previously obtained so that a portion of the shared memory is available for any other threads that want to use it. In this way, it is evident that the impossibility of incurring races is critical as the need of the lock for the thread requires that at a given instant, only a given thread can use this part of the shared...

If we want only the thread that acquires a lock to release it, we must use a RLock() object. Similar to the Lock() object, the RLock() object has two methods: acquire() and release(). RLock() is useful when you want to have a thread-safe access from outside the class and use the same methods from inside the class.

import threading
import time

class Box(object):
    lock = threading.RLock()
    def __init__(self):
        self.total_items = 0
    def execute(self,n):
        Box.lock.acquire()
        self.total_items += n
        Box.lock.release()
    def add(self):
        Box.lock.acquire()
        self.execute(1)
        Box.lock.release()
    def remove(self...