Eventual vs Strong Consistency

When building distributed systems, one of the biggest challenges is keeping data consistent across multiple servers. You have two main options: Strong Consistency and Eventual Consistency. Let's understand when to use each approach.

Strong Consistency ensures that all servers have the exact same data at all times. When you write data to one server, the system waits until all other servers have updated before confirming the write.

While Strong Consistency gives accurate data, it comes with a cost. The system has to wait for all servers to respond, which makes writes slower. If some servers are down or slow to respond, the whole system slows down. This approach works well for systems that need absolute accuracy, like banking transactions.

Eventual Consistency takes a different approach. It allows servers to have different versions of data temporarily, but ensures they all match up eventually. When you write data to one server, the system confirms the write immediately and updates other servers in the background. This makes writes much faster since you don't have to wait for all servers to sync.

The trade-off is that different users might see different versions of data for a short time. For example, when you post on social media, some of your followers might see your post immediately while others see it a few seconds later. This is fine for many applications where perfect accuracy isn't critical.

Here's a comparison of both approaches: