The CAP theorem and the principles that drive CRDTs closely tie in distributed systems. The theorem is a fundamental element when discussing data replication and consistency mechanisms, explaining why we can't design a perfect system that guarantees consistency, availability and partition tolerance all the time. To relate this to CRDTs, let's look at CRDTs through the CAP lens.

In terms of consistency, CRDTs offer eventual consistency, where state-based CRDTs converge to the same state as all updates are applied, even if the order is different. This might not provide strict consistency (every read receives the most recent write), but it mitigates conflict in a network with high latency or partial partitions. As per the CAP theorem, going for eventual consistency allows us to strive for better availability and partition tolerance.

In terms of availability, since CRDTs ensure that any state or operation transferred to other replicas will not cause conflicts, operations can always be executed without waiting for communication with other replicas. This means higher availability (every request receives a response), aligning with the A in CAP.

In terms of partition tolerance, CRDTs can clearly tolerate network partitions as they work on the principle of local changes converging to a common state across all replicas. Thus, even in the event of a network split, your system will continue functioning (the system functions even during network partitions), ticking the last box in CAP.

The interaction between CRDTs and the CAP theorem provides insights into trade-offs made when designing distributed systems.

Consider the provided Python script as our revisit of the movie marathon list. Assume these operations are being executed in a distributed system suffering from latency issues. As the CAP theorem states, we can't have perfect consistency, availability, and partition tolerance together; due to this, we have decided to give preference to availability and partition tolerance, accepting the notion of eventual consistency, which is what CRDTs provide.

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if __name__ == "__main__":

  # The favorite movies list (the state in our CRDT)

  movies = ['Inception', 'The Dark Knight', 'Interstellar']

  replicas = [movies.copy() for _ in range(3)]  # list replicated on three machines

​

  # New movie suggested by a friend

  new_movie = 'Dunkirk'

​

  # Due to network latency, this update is applied to only one machine initially

  # No coordination with other replicas is required for this local change (availability)

  replicas[0].append(new_movie)

​

  # Eventually, the update should reach the other machines (eventual consistency)

  # Even during this slow update propagation, the system keeps functioning (partition tolerance)

  for i in range(1, len(replicas)):

    replicas[i].append(new_movie)

​

  print('Updated Movie List on all replicas:', replicas)