People want everyone to see the same data in distributed systems, but it's also important not to sacrifice performance. So, what are the differences between consistency models? For example, what's the fundamental logic behind strong consistency versus eventual consistency? Which one do you think we should prefer?
How is data consistency ensured in distributed systems?
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Comparing eventual consistency to grocery delivery in an order system is, in my opinion, the clearest example. When you place a grocery order, the cashier doesn’t say, “Your order will be delivered as soon as possible,” they simply confirm, “Your order has been received.” It doesn’t matter if you and the warehouse don’t see the exact same data at that exact moment—what matters is that *eventually*, everyone sees the same data. That’s exactly how eventual consistency works: when a piece of data changes, it isn’t synchronized immediately but rather when the system is idle, and eventually, everyone sees the same version. It’s unmatched in performance because it eliminates waiting time and tolerates delays in distributed systems.
You can liken strong consistency to withdrawing money from an ATM. The moment you insert your card, the system instantly checks whether you have sufficient funds and gives you the result. If your balance is insufficient, the transaction doesn’t go through because the entire system’s data has been updated and made consistent in real time. This model comes with a performance cost because the system must wait for all nodes to synchronize with every transaction. However, it’s mandatory in scenarios where data accuracy is critical, such as banking or aviation systems. In short, the choice depends entirely on the scenario: if speed and scalability are priorities and temporary inconsistencies are acceptable, go with eventual consistency; if everyone needs to see the same data at the same time, strong consistency is the way to go.