Strong vs. Eventual Consistency – Which Consistency Model Fits Your System?
Strong Consistency means that once a write operation is acknowledged, all subsequent reads will reflect that most recent write. In other words, your system behaves as if there’s only one copy of the data—everyone sees the same state instantly.
Pros:
Predictable read behavior—no stale data.
Easier mental model for application developers.
Cons:
Higher Latency—coordinating across nodes can be slow.
Reduced Availability—if one node is down, you might have to wait until it’s back to guarantee consistency.
Eventual Consistency ensures that, given enough time, all nodes in the system will converge to the same value, but it doesn’t guarantee when. If you read data immediately after a write, you might get older data on some nodes.
Pros:
High Availability—nodes can accept reads/writes even if others are offline.
Low Latency—no need to coordinate with every node for each write.
Cons:
Inconsistent Reads—some nodes may lag behind.
Complex error handling if your application needs strict ordering or up-to-date data.