The Fallacy of Perfect Consistency

In the world of distributed systems, we often chase the holy grail of Strong Consistency. We want our data to be everywhere, instantly, and perfectly synchronized. But the universe, and the speed of light, has other plans.

The CAP Theorem Revisited

You know the drill: Consistency, Availability, Partition Tolerance. Pick two. But in reality, Partition Tolerance is not optional. Networks fail. Cables get cut. Sharks bite undersea fiber optics.

“A distributed system is one in which the failure of a computer you didn’t even know existed can render your own computer unusable.” - Leslie Lamport

Why I Choose Availability

For most user-facing applications, availability is king. If a user can’t add an item to their cart because a replica in us-east-1 is lagging, you lose money. Eventual consistency is not just a compromise; it’s a realistic acceptance of the physical world.

func HandleRequest(w http.ResponseWriter, r *http.Request) {
    // We accept the write immediately
    go replicateToFollowers(data)
    w.WriteHeader(http.StatusOK)
}

This simple pattern, while dangerous if not handled correctly, powers much of the modern web.

Conclusion

Embrace the chaos. Design for failure. And maybe, just maybe, your system will survive the next shark attack.