We now turn our direction towards exploring consistency strategies in distributed systems, focusing on the eventual consistency model. Imagine directing a movie where your actors are in different parts of the world. Our script is the database, and each actor represents a node.

You send script changes for the 'Opening Scene' to each of them. Each actor updates their script independently without the need for a central coordinating process (much like how actors in Fight Club played their parts in widespread locations). Therefore, the 'changes' are updates in our distributed database, and the actors are the database nodes.

With eventual consistency, you trust that everyone gets the update in their own time – and so at a certain point in time, all actors have an updated script and are ready to perform their parts. Similarly, all nodes will reach a state of consistency at some point, even though they update independently, just like the actors updating their scripts.

Here's a Python code snippet that simulates the process: In this code snippet, each actor represent a node. The process of sending each actor the script for the 'Opening Scene' and then waiting until all actors have updated their scripts represents the principle of eventual consistency in distributed databases.

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if __name__ == '__main__':

  # Imagine you as the movie director

  scene = 'Opening Scene'

  actors=['Actor 1', 'Actor 2', 'Actor 3']

  for actor in actors:

    print(f'{actor} has now received the script for {scene}')

  print('All actors have updated their scripts')