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In this example, we will create a task queue that holds all the tasks to be executed and a thread pool that interacts with the queue to process its elements individually.

We will begin with the question, what is a Queue?. A queue is a data structure that is a collection of different elements maintained in a very specific order. Let me explain by taking a real life example.

Assume you stand in line to pay your grocery billat a grocery shop counter, (don't ask me which grocery shop)

In a line of people waiting to pay their bills, you will notice the following:

1. People enter at one end of the line and exit from the other end.

2. If person A enters the line before person B, person A will leave the line before person B (unless person B is a celebrity or has more priority).

3. Once everyone has paid their bills, there will be no one left in the line.

Well, back to programming where a queue works in a similar fashion.

1. enqueue - Elements added to the end of the queue.

2. dequeue - Elements removed from the beginning of the queue.

There is more, First In First Out (FIFO) - elements that are added first will be removed first. Last In First Out (LIFO) - last element that is added will be removed first.

The queue module in Python provides a simple implementation of the queue data structure. Each queue can have the following methods.

get(): returns the next element.

put(): adds a new element.

qsize(): number of current elements in queue.

empty(): returns a Boolean, indicating whether the queue is empty.

full(): returns a Boolean, indicating whether the queueis full.

1. We will create a function which takes an argument x then iterates through the numbers between 1 and itself(x), to perform multiplication. For e.g. when you pass 5 to this function it iterates through 1 to 5 and keep multiplying i.e. 1 times 5, 2 times 5, 3 times 5, 4 times 5, 5 times 5 finally returning the values as a list.

def print_multiply(x): output_value = [] for i in range(1, x + 1): output_value.append(i * x) print(f"Output \n *** The multiplication result for the {x} is - {output_value}") print_multiply(5)

*** The multiplication result for the 5 is - [5, 10, 15, 20, 25]

2. We will write another function called process_queue() which will attempt to obtain the next element of the queue object. The logic for this quite simple, keep passing the elements until the queue is empty. I will use sleep to delay proceeding a bit.

def process_queue(): while True: try: value = my_queue.get(block=False) except queue.Empty: return else: print_multiply(value) time.sleep(2)

3. Create a class, when a new instance is initialized and started, the process_queue() function will be called.

class MultiThread(threading.Thread): def __init__(self, name): threading.Thread.__init__(self) self.name = name def run(self): print(f" ** Starting the thread - {self.name}") process_queue() print(f" ** Completed the thread - {self.name}")

4. Finally, we will pass the input list of numbers and fill the queue.

# setting up variables input_values = [5, 10, 15, 20] # fill the queue my_queue = queue.Queue() for x in input_values: my_queue.put(x)

5. Finally, putting all together.

import queue import threading import time # Class class MultiThread(threading.Thread): def __init__(self, name): threading.Thread.__init__(self) self.name = name def run(self): print(f"Output \n ** Starting the thread - {self.name}") process_queue() print(f" ** Completed the thread - {self.name}") # Process thr queue def process_queue(): while True: try: value = my_queue.get(block=False) except queue.Empty: return else: print_multiply(value) time.sleep(2) # function to multiply def print_multiply(x): output_value = [] for i in range(1, x + 1): output_value.append(i * x) print(f" \n *** The multiplication result for the {x} is - {output_value}") # Input variables input_values = [2, 4, 6, 5,10,3] # fill the queue my_queue = queue.Queue() for x in input_values: my_queue.put(x) # initializing and starting 3 threads thread1 = MultiThread('First') thread2 = MultiThread('Second') thread3 = MultiThread('Third') thread4 = MultiThread('Fourth') # Start the threads thread1.start() thread2.start() thread3.start() thread4.start() # Join the threads thread1.join() thread2.join() thread3.join() thread4.join()

** Starting the thread - First *** The multiplication result for the 2 is - [2, 4]

** Starting the thread - Second *** The multiplication result for the 4 is - [4, 8, 12, 16]

** Starting the thread - Third *** The multiplication result for the 6 is - [6, 12, 18, 24, 30, 36]

** Starting the thread - Fourth *** The multiplication result for the 5 is - [5, 10, 15, 20, 25] *** The multiplication result for the 10 is - [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] *** The multiplication result for the 3 is - [3, 6, 9] ** Completed the thread - Third ** Completed the thread - Fourth ** Completed the thread - Second ** Completed the thread - First

6. We have successfully implemented queue concept. See, we have 4 threads but there are 6 values to process, so whoever comes first to the Queue will be executed and others will be in line to wait for others to complete.

This is similar to a real life, assume there are 3 counters but 10 people waiting to pay their bills so 10 people will be in 3 queues and who ever have completed paying the bills will leave the line and make way for next person.

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