With the ever increasing demand for a fast application, sometimes the traditional procedural approaches just might not work. I was faced with a feature, which required handling lots of data, I did iteratively try so many procedural approaches ensuring I sorted efficiently, but that wasn't enough, for weeks or months this went on, until I had this thought in my head of seeing each query as a Needle's thread, this brought about Concurrency. Over the course of this tutorial, I will be providing more context on my thought process leading right until the Concurrency solution and right after, the time saved and several other details. Majorly, a breakdown of what we'll be looking at is - What is Concurrency (in simple terms)? - What is Parallelism (in simple terms), and how they are differentiated from Concurrency? - Background Story, providing context on the feature I was working on and the solutions I used - Brief deep-dive into a code showing how to implement one or rather How I implemented one e.g

 # importing the Library
 from multiprocessing.pool import ThreadPool as Pool

 # a class utilizing the pool library
 class ObjectUtilizingThreadPool():

     # one of the processes to be used
     def one_process(self):
         pass
     # one of the processes to be used
     def two_process(self):
         pass
     # the method implementing concurrency
     def pool_process(self):

         with Pool(processes=3) as pool:
             proc_one = pool.apply_async(self.one_process())
             res1 = proc_one.get()
             proc_two = pool.apply_async(self.two_process())
             res2 = pool.apply_async(self.two_process())
         res = res1 + res2
         return res

• Use-cases of the concept
• Beyond the basics