Consistency patterns in distributed systems are strategies used to manage the trade-offs between consistency, availability, and partition tolerance as described by the CAP theorem. Here are some key consistency patterns:
Strong Consistency
Strong consistency ensures that all nodes in a distributed system have the same view of data at all times. This involves synchronous communication and coordination between nodes, which can lead to increased latency and reduced availability.
Linearizability (Atomic Consistency): Every operation appears to take effect instantly at a single point in time, creating a linear ordering of operations as if they were executed serially.
Serializability: Transactions appear to execute one after another, even in a distributed environment, ensuring that the system behaves as if it were executing transactions in a serial order[5].
Eventual Consistency
Eventual consistency allows data to be temporarily inconsistent across nodes but guarantees that all nodes will eventually converge to a consistent state. This pattern is often used in systems where strong consistency would lead to excessive latency or limited availability.
Read-your-writes Consistency: Any read operation following a write operation should return the value written by that operation.
Monotonic Reads and Monotonic Writes Consistency: The system ensures that once a value is read, subsequent reads will never return older values. Similarly, writes are committed in order[5].
Causal Consistency
Causal consistency maintains a causal relationship between related events, ensuring that ...
